Semiparametric Theory and Missing Data

Springer Series in Statistics Advisors: P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, S. Zeger Springer Seri...

Author: Anastasios Tsiatis

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Springer Series in Statistics Advisors: P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, S. Zeger

Springer Series in Statistics Alho/Spencer: Statistical Demography and Forecasting. Andersen/Borgan/Gill/Keiding: Statistical Models Based on Counting Processes. Atkinson/Riani: Robust Diagnostic Regression Analysis. Atkinson/Riani/Cerioli: Exploring Multivariate Data with the Forward Search. Berger: Statistical Decision Theory and Bayesian Analysis, 2nd edition. Borg/Groenen: Modern Multidimensional Scaling: Theory and Applications, 2nd edition. Brockwell/Davis: Time Series: Theory and Methods, 2nd edition. Bucklew: Introduction to Rare Event Simulation. Cappé/Moulines/Rydén: Inference in Hidden Markov Models. Chan/Tong: Chaos: A Statistical Perspective. Chen/Shao/Ibrahim: Monte Carlo Methods in Bayesian Computation. Coles: An Introduction to Statistical Modeling of Extreme Values. David/Edwards: Annotated Readings in the History of Statistics. Devroye/Lugosi: Combinatorial Methods in Density Estimation. Efromovich: Nonparametric Curve Estimation: Methods, Theory, and Applications. Eggermont/LaRiccia: Maximum Penalized Likelihood Estimation, Volume I: Density Estimation. Fahrmeir/Tutz: Multivariate Statistical Modelling Based on Generalized Linear Models, 2nd edition. Fan/Yao: Nonlinear Time Series: Nonparametric and Parametric Methods. Farebrother: Fitting Linear Relationships: A History of the Calculus of Observations 1750-1900. Federer: Statistical Design and Analysis for Intercropping Experiments, Volume I: Two Crops. Federer: Statistical Design and Analysis for Intercropping Experiments, Volume II: Three or More Crops. Ferraty/Vieu: Nonparametric Functional Data Analysis: Models, Theory, Applications, and Implementation Ghosh/Ramamoorthi: Bayesian Nonparametrics. Glaz/Naus/Wallenstein: Scan Statistics. Good: Permutation Tests: Parametric and Bootstrap Tests of Hypotheses, 3rd edition. Gouriéroux: ARCH Models and Financial Applications. Gu: Smoothing Spline ANOVA Models. Györfi/Kohler/Krzyz• ak/Walk: A Distribution-Free Theory of Nonparametric Regression. Haberman: Advanced Statistics, Volume I: Description of Populations. Hall: The Bootstrap and Edgeworth Expansion. Härdle: Smoothing Techniques: With Implementation in S. Harrell: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Hart: Nonparametric Smoothing and Lack-of-Fit Tests. Hastie/Tibshirani/Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Hedayat/Sloane/Stufken: Orthogonal Arrays: Theory and Applications. Heyde: Quasi-Likelihood and its Application: A General Approach to Optimal Parameter Estimation. (continued after index)

Anastasios A. Tsiatis

Semiparametric Theory and Missing Data

Anastasios A. Tsiatis Department of Statistics North Carolina State University Raleigh, NC 27695 USA [email protected]

Library of Congress Control Number: 2006921164 ISBN-10: 0-387-32448-8 ISBN-13: 978-0387-32448-7 Printed on acid-free paper. © 2006 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Springer Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. 9 8 7 6 5 4 3 2 1 springer.com

(MVY)

To My Mother, Anna My Wife, Marie and My Son, Greg

Preface

Missing data are prevalent in many studies, especially when the studies involve human beings. Not accounting for missing data properly when analyzing data can lead to severe biases. For example, most software packages, by default, delete records for which any data are missing and conduct the so-called “complete-case analysis”. In many instances, such an analysis will lead to an incorrect inference. Since the 1980s there has been a serious attempt to understand the underlying issues involved with missing data. In this book, we study the diﬀerent mechanisms for missing data and some of the diﬀerent analytic strategies that have been suggested in the literature for dealing with such problems. A special case of missing data includes censored data, which occur frequently in the area of survival analysis. Some discussion of how the missing-data methods that are developed will apply to problems with censored data is also included. Underlying any missing-data problem is the statistical model for the data if none of the data were missing (i.e., the so-called full-data model). In this book, we take a very general approach to statistical modeling. That is, we consider statistical models where interest focuses on making inference on a ﬁnite set of parameters when the statistical model consists of the parameters of interest as well as other nuisance parameters. Unlike most traditional statistical models, where the nuisance parameters are ﬁnite-dimensional, we consider the more general problem of inﬁnite-dimensional nuisance parameters. This allows us to develop theory for important statistical methods such as regression models that model the conditional mean of a response variable as a function of covariates without making any additional distributional assumptions on the variables and the proportional hazards regression model for survival data. Models where the parameters of interest are ﬁnite-dimensional and the nuisance parameters are inﬁnite-dimensional are called semiparametric models. The ﬁrst ﬁve chapters of the book consider semiparametric models when there are no missing data. In these chapters, semiparametric models are deﬁned and some of the theoretical developments for estimators of the parame-

viii

Preface

ters in these models are reviewed. The semiparametric theory and the properties of the estimators for parameters in semiparametric models are developed from a geometrical perspective. Consequently, in Chapter 2, a quick review of the geometry of Hilbert spaces is given. The geometric ideas are ﬁrst developed for ﬁnite-dimensional parametric models in Chapter 3 and then extended to inﬁnite-dimensional models in Chapters 4 and 5. A rigorous treatment of semiparametric theory is given in the book Efﬁcient and Adaptive Estimation for Semiparametric Models by Bickel et al. (1993). (Johns Hopkins University Press: Baltimore, MD). My experience has been that this book is too advanced for many students in statistics and biostatistics even at the Ph.D. level. The attempt here is to be more expository and heuristic, trying to give an intuition for the basic ideas without going into all the technical details. Although the treatment of this subject is not rigorous, it is not trivial either. Readers should not be frustrated if they don’t grasp all the concepts at ﬁrst reading. This ﬁrst part of the book that deals only with semiparametric models (absent missing data) and the geometric theory of semiparametrics will be important in its own right. It is a beautiful theory, where the geometric perspective gives a new insight and deeper understanding of statistical models and the properties of estimators for parameters in such models. The remainder of the book focuses on missing-data methods, building on the semiparametric techniques developed in the earlier chapters. In Chapter 6, a discussion and overview of missing-data mechanisms is given. This includes the deﬁnition and motivation for the three most common categories of missingness, namely • • •

missing completely at random (MCAR) missing at random (MAR) nonmissing at random (NMAR)

These ideas are extended to the broader class of coarsened data. We show how statistical models for full data can be integrated with missingness or coarsening mechanisms that allow us to derive likelihoods and models for the observed data in the presence of missingness. The geometric ideas for semiparametric full-data models are extended to missing-data models. This treatment will give the reader a deep understanding of the underlying theory for missing and coarsened data. Methods for estimating parameters with missing or coarsened data in as eﬃcient a manner as possible are emphasized. This theory leads naturally to inverse probability weighted complete-case (IPWCC) and augmented inverse probability weighted complete-case (AIPWCC) estimators, which are discussed in great detail in Chapters 7 through 11. As we will see, some of the proposed methods can become computationally challenging if not infeasible. Therefore, in Chapter 12, we give some approximate methods for obtaining more eﬃcient estimators with missing data that are easier to implement. Much of the theory developed in this book is taken from a series of

Preface

ix

ground-breaking papers by Robins and Rotnitzky (together with colleagues), who developed this elegant semiparametric theory for missing-data problems. A short discussion on how missing-data semiparametric methods can be applied to estimate causal treatment eﬀects in a point exposure study is given in Chapter 13 to illustrate the broad applicability of these methods. In Chapter 14, the ﬁnal chapter, we deviate slightly from semiparametric models to discuss some of the theoretical properties of multiple-imputation estimators for ﬁnite-dimensional parametric models. However, even here, the theory developed throughout the book will be useful in understanding the properties of such estimators.

Anastasios (Butch) Tsiatis

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1

Introduction to Semiparametric Models . . . . . . . . . . . . . . . . . . . 1.1 What Is an Inﬁnite-Dimensional Space? . . . . . . . . . . . . . . . . . . . . 1.2 Examples of Semiparametric Models . . . . . . . . . . . . . . . . . . . . . . . Example 1: Restricted Moment Models . . . . . . . . . . . . . . . . . . . . . Example 2: Proportional Hazards Model . . . . . . . . . . . . . . . . . . . . Example 3: Nonparametric Model . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Semiparametric Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 3 3 7 8 8

2

Hilbert Space for Random Vectors . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The Space of Mean-Zero q-dimensional Random Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Dimension of the Space of Mean-Zero Random Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Hilbert Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Linear Subspace of a Hilbert Space and the Projection Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Projection Theorem for Hilbert Spaces . . . . . . . . . . . . . . . . . . . . . 2.4 Some Simple Examples of the Application of the Projection Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example 1: One-Dimensional Random Functions . . . . . . . . . . . . Example 2: q-dimensional Random Functions . . . . . . . . . . . . . . . 2.5 Exercises for Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

The Geometry of Inf luence Functions . . . . . . . . . . . . . . . . . . . . . . 3.1 Super-Eﬃciency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example Due to Hodges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 m-Estimators (Quick Review) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimating the Asymptotic Variance of an m-Estimator . . . . . . Proof of Theorem 3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 24 24 29 31 34

3

11 12 13 14 14 15 15 16 19

xii

4

5

Contents

3.3 Geometry of Inﬂuence Functions for Parametric Models . . . . . . Constructing Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Eﬃcient Inﬂuence Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic Variance when Dimension Is Greater than One . . . Geometry of Inﬂuence Functions . . . . . . . . . . . . . . . . . . . . . . . . . . Deriving the Eﬃcient Inﬂuence Function . . . . . . . . . . . . . . . . . . . 3.5 Review of Notation for Parametric Models . . . . . . . . . . . . . . . . . . 3.6 Exercises for Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38 38 42 43 45 46 49 50

Semiparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 GEE Estimators for the Restricted Moment Model . . . . . . . . . . Asymptotic Properties for GEE Estimators . . . . . . . . . . . . . . . . . Example: Log-linear Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Parametric Submodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Inﬂuence Functions for Semiparametric RAL Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Semiparametric Nuisance Tangent Space . . . . . . . . . . . . . . . . . . . Tangent Space for Nonparametric Models . . . . . . . . . . . . . . . . . . . Partitioning the Hilbert Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Semiparametric Restricted Moment Model . . . . . . . . . . . . . . . . . . The Space Λ2s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Space Λ1s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inﬂuence Functions and the Eﬃcient Inﬂuence Function for the Restricted Moment Model . . . . . . . . . . . . . . . . . . . . . . . The Eﬃcient Inﬂuence Function . . . . . . . . . . . . . . . . . . . . . . . . . . . A Diﬀerent Representation for the Restricted Moment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existence of a Parametric Submodel for the Arbitrary Restricted Moment Model . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Adaptive Semiparametric Estimators for the Restricted Moment Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensions of the Restricted Moment Model . . . . . . . . . . . . . . . . 4.7 Exercises for Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 54 55 57 59 61 63 68 69 73 77 79 83 85 87 91 93 97 98

Other Examples of Semiparametric Models . . . . . . . . . . . . . . . . 101 5.1 Location-Shift Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . 101 The Nuisance Tangent Space and Its Orthogonal Complement for the Location-Shift Regression Model . . . . . . . . . . . . . . 103 Semiparametric Estimators for β . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Eﬃcient Score for the Location-Shift Regression Model . . . . . . . 107 Locally Eﬃcient Adaptive Estimators . . . . . . . . . . . . . . . . . . . . . . 108 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5.2 Proportional Hazards Regression Model with Censored Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 The Nuisance Tangent Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Contents

5.3 5.4

5.5 5.6

xiii

The Space Λ2s Associated with λC|X (v|x) . . . . . . . . . . . . . . . . . . 117 The Space Λ1s Associated with λ(v) . . . . . . . . . . . . . . . . . . . . . . . 119 Finding the Orthogonal Complement of the Nuisance Tangent Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Finding RAL Estimators for β . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Eﬃcient Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Estimating the Mean in a Nonparametric Model . . . . . . . . . . . . . 125 Estimating Treatment Diﬀerence in a Randomized Pretest-Posttest Study or with Covariate Adjustment . . . . . . . . 126 The Tangent Space and Its Orthogonal Complement . . . . . . . . . 129 Remarks about Auxiliary Variables . . . . . . . . . . . . . . . . . . . . . . . . 133 Exercises for Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

6

Models and Methods for Missing Data . . . . . . . . . . . . . . . . . . . . . 137 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Likelihood Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.3 Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 6.4 Inverse Probability Weighted Complete-Case Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 6.5 Double Robust Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.6 Exercises for Chapter 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

7

Missing and Coarsening at Random for Semiparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 7.1 Missing and Coarsened Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Missing Data as a Special Case of Coarsening . . . . . . . . . . . . . . . 153 Coarsened-Data Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7.2 The Density and Likelihood of Coarsened Data . . . . . . . . . . . . . . 156 Discrete Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Continuous Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Likelihood when Data Are Coarsened at Random . . . . . . . . . . . . 158 Brief Remark on Likelihood Methods . . . . . . . . . . . . . . . . . . . . . . 160 Examples of Coarsened-Data Likelihoods . . . . . . . . . . . . . . . . . . . 161 7.3 The Geometry of Semiparametric Coarsened-Data Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 The Nuisance Tangent Space Associated with the Full-Data Nuisance Parameter and Its Orthogonal Complement . . 166 7.4 Example: Restricted Moment Model with Missing Data by Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 The Logistic Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 7.5 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 7.6 Exercises for Chapter 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

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8

The Nuisance Tangent Space and Its Orthogonal Complement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 8.1 Models for Coarsening and Missingness . . . . . . . . . . . . . . . . . . . . . 185 Two Levels of Missingness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Monotone and Nonmonotone Coarsening for more than Two Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 8.2 Estimating the Parameters in the Coarsening Model . . . . . . . . . 188 MLE for ψ with Two Levels of Missingness . . . . . . . . . . . . . . . . . 188 MLE for ψ with Monotone Coarsening . . . . . . . . . . . . . . . . . . . . . 189 8.3 The Nuisance Tangent Space when Coarsening Probabilities Are Modeled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 8.4 The Space Orthogonal to the Nuisance Tangent Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 8.5 Observed-Data Inﬂuence Functions . . . . . . . . . . . . . . . . . . . . . . . . 193 8.6 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 8.7 Exercises for Chapter 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

9

Augmented Inverse Probability Weighted Complete-Case Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 9.1 Deriving Semiparametric Estimators for β . . . . . . . . . . . . . . . . . . 199 Interesting Fact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 Estimating the Asymptotic Variance . . . . . . . . . . . . . . . . . . . . . . . 206 9.2 Additional Results Regarding Monotone Coarsening . . . . . . . . . 207 The Augmentation Space Λ2 with Monotone Coarsening . . . . . . 207 9.3 Censoring and Its Relationship to Monotone Coarsening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Probability of a Complete Case with Censored Data . . . . . . . . . 216 The Augmentation Space, Λ2 , with Censored Data . . . . . . . . . . . 216 Deriving Estimators with Censored Data . . . . . . . . . . . . . . . . . . . 217 9.4 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 9.5 Exercises for Chapter 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

10 Improving Eﬃciency and Double Robustness with Coarsened Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.1 Optimal Observed-Data Inﬂuence Function Associated with Full-Data Inﬂuence Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.2 Improving Eﬃciency with Two Levels of Missingness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Finding the Projection onto the Augmentation Space . . . . . . . . 226 Adaptive Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Algorithm for Finding Improved Estimators with Two Levels of Missingness . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Remarks Regarding Adaptive Estimators . . . . . . . . . . . . . . . . . . . 230 Estimating the Asymptotic Variance . . . . . . . . . . . . . . . . . . . . . . . 233 Double Robustness with Two Levels of Missingness . . . . . . . . . . 234

Contents

10.3

10.4 10.5

10.6 10.7

xv

Remarks Regarding Double-Robust Estimators . . . . . . . . . . . . . . 236 Logistic Regression Example Revisited . . . . . . . . . . . . . . . . . . . . . 236 Improving Eﬃciency with Monotone Coarsening . . . . . . . . . . . . . 239 Finding the Projection onto the Augmentation Space . . . . . . . . 239 Adaptive Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Double Robustness with Monotone Coarsening . . . . . . . . . . . . . . 248 Example with Longitudinal Data . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Remarks Regarding Right Censoring . . . . . . . . . . . . . . . . . . . . . . . 254 Improving Eﬃciency when Coarsening Is Nonmonotone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Finding the Projection onto the Augmentation Space . . . . . . . . 256 Uniqueness of M−1 (·) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Obtaining Improved Estimators with Nonmonotone Coarsening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Double Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Exercises for Chapter 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Example: Estimating the Mean with Missing Data . . . . . . . . . . . 275 11.1 The Observed-Data Eﬃcient Score . . . . . . . . . . . . . . . . . . . . . . . . . 277 Representation 1 (Likelihood-Based) . . . . . . . . . . . . . . . . . . . . . . . 277 Representation 2 (AIPWCC-Based) . . . . . . . . . . . . . . . . . . . . . . . . 278 Relationship between the Two Representations . . . . . . . . . . . . . . 278 M−1 for Monotone Coarsening . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 M−1 with Right Censored Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 11.2 Strategy for Obtaining Improved Estimators . . . . . . . . . . . . . . . . 285 Example: Restricted Moment Model with Monotone Coarsening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Some Brief Remarks Regarding Robustness . . . . . . . . . . . . . . . . . 290 11.3 Concluding Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 11.4 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 11.5 Exercises for Chapter 11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 12 Approximate Methods for Gaining Eﬃciency . . . . . . . . . . . . . . 295 12.1 Restricted Class of AIPWCC Estimators . . . . . . . . . . . . . . . . . . . 295 12.2 Optimal Restricted (Class 1) Estimators . . . . . . . . . . . . . . . . . . . . 300 Deriving the Optimal Restricted (Class 1) AIPWCC Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Estimating the Asymptotic Variance . . . . . . . . . . . . . . . . . . . . . . . 307 12.3 Example of an Optimal Restricted (Class 1) Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Modeling the Missingness Probabilities . . . . . . . . . . . . . . . . . . . . . 312 12.4 Optimal Restricted (Class 2) Estimators . . . . . . . . . . . . . . . . . . . . 313

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Contents

Logistic Regression Example Revisited . . . . . . . . . . . . . . . . . . . . . 319 12.5 Recap and Review of Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 12.6 Exercises for Chapter 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 13 Double-Robust Estimator of the Average Causal Treatment Eﬀect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 13.1 Point Exposure Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 13.2 Randomization and Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 13.3 Observational Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 13.4 Estimating the Average Causal Treatment Eﬀect . . . . . . . . . . . . 328 Regression Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 13.5 Coarsened-Data Semiparametric Estimators . . . . . . . . . . . . . . . . 329 Observed-Data Inﬂuence Functions . . . . . . . . . . . . . . . . . . . . . . . . 331 Double Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 13.6 Exercises for Chapter 13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 14 Multiple Imputation: A Frequentist Perspective . . . . . . . . . . . 339 14.1 Full- Versus Observed-Data Information Matrix . . . . . . . . . . . . . 342 14.2 Multiple Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 14.3 Asymptotic Properties of the Multiple-Imputation Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Stochastic Equicontinuity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 14.4 Asymptotic Distribution of the Multiple-Imputation Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 14.5 Estimating the Asymptotic Variance . . . . . . . . . . . . . . . . . . . . . . . 362 Consistent Estimator for the Asymptotic Variance . . . . . . . . . . . 365 14.6 Proper Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Asymptotic Distribution of n1/2 (βˆn∗ − β0 ) . . . . . . . . . . . . . . . . . . . 367 Rubin’s Estimator for the Asymptotic Variance . . . . . . . . . . . . . 370 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 14.7 Surrogate Marker Problem Revisited . . . . . . . . . . . . . . . . . . . . . . . 371 How Do We Sample? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

1 Introduction to Semiparametric Models

Statistical problems are described using probability models. That is, data are envisioned as realizations of a vector of random variables Z1 , . . . , Zn , where Zi itself is a vector of random variables corresponding to the data collected on the i-th individual in a sample of n individuals chosen from some population of interest. We will assume throughout the book that Z1 , . . . , Zn are identically and independently distributed (iid) with density belonging to some probability (or statistical model), where a model consists of a class of densities that we believe might have generated the data. The densities in a model are often identiﬁed through a set of parameters; i.e., a real-valued vector used to describe the densities in a statistical model. The problem is usually set up in such a way that the value of the parameters or, at the least, the value of some subset of the parameters that describes the density that generates the data, is of importance to the investigator. Much of statistical inference considers how we can learn about this “true” parameter value from a sample of observed data. Models that are described through a vector of a ﬁnite number of real values are referred to as ﬁnite-dimensional parametric models. For ﬁnite-dimensional parametric models, the class of densities can be described as P = {p(z, θ), θ ∈ Ω ⊂ Rp }, where the dimension p is some ﬁnite positive integer. For many problems, we are interested in making inference only on a subset of the parameters. Nonetheless, the entire set of parameters is necessary to properly describe the class of possible distributions that may have generated the data. Suppose, for example, we are interested in estimating the mean response of a variable, which we believe follows a normal distribution. Typically, we conduct an experiment where we sample from that distribution and describe the data that result from that experiment as a realization of the random vector Z1 , . . . , Zn assumed iid N (µ, σ 2 ); µ ∈ R, σ 2 > 0; θ = (µ, σ 2 ) ∈ R × R+ ⊂ R2 .

2

1 Introduction to Semiparametric Models

Here we are interested in estimating µ, the mean of the distribution, but σ 2 , the variance of the distribution, is necessary to properly describe the possible probability distributions that might have generated the data. It is useful to write the parameter θ as (β T , η T )T , where β q×1 (a q-dimensional vector) is the parameter of interest and η r×1 (an r-dimensional vector) is the nuisance parameter. In the previous example, β = µ and η = σ 2 . The entire parameter space Ω has dimension p = q + r. In some cases, we may want to consider models where the class of densities is so large that the parameter θ is inﬁnite-dimensional. Examples of this will be given shortly. For such models, we will consider the problem where we are interested in estimating β, which we still take to be ﬁnite-dimensional, say q-dimensional. For some problems, it will be natural to partition the parameter θ as (β, η), where β is the q-dimensional parameter of interest and η is the nuisance parameter, which is inﬁnite-dimensional. In other cases, it is more natural to consider the parameter β as the function β(θ). These models are referred to as semiparametric models in the literature because, generally, there is both a parametric component β and a nonparametric component η that describe the model. By allowing the space of parameters to be inﬁnitedimensional, we are putting less restrictions on the probabilistic constraints that our data might have (compared with ﬁnite-dimensional parametric models). Therefore, solutions, if they exist and are reasonable, will have greater applicability and robustness. Because the notion of an inﬁnite-dimensional parameter space is so important in the subsequent development of this book, we start with a short discussion of inﬁnite-dimensional spaces.

1.1 What Is an Inﬁnite-Dimensional Space? The parameter spaces that we will consider in this book will always be subsets of linear vector spaces. That is, we will consider a parameter space Ω ⊂ S, where S is a linear space. A space S is a linear space if, for θ1 and θ2 elements of S, aθ1 + bθ2 will also be an element of S for any scalar constants a and b. Such a linear space is ﬁnite-dimensional if it can be spanned by a ﬁnite number of elements in the space. That is, S is a ﬁnite-dimensional linear space if elements θ1 , . . . , θm exist, where m is some ﬁnite positive integer such that any element θ ∈ S is equal to some linear combination of θ1 , . . . , θm ; i.e., θ = a1 θ1 + . . . + am θm for some scalar constants a1 , . . . , am . The dimension of a ﬁnite-dimensional linear space is deﬁned by the minimum number of elements in the space that span the entire space or, equivalently, the number of linearly independent elements that span the entire space, where a set of elements are linearly independent if no element in the set can be written as a linear combination of the other elements. Parameter spaces that are deﬁned in p-dimensional Euclidean spaces are clearly ﬁnite-dimensional spaces. A linear

1.2 Examples of Semiparametric Models

3

space S that cannot be spanned by any ﬁnite set of elements is called an inﬁnite-dimensional parameter space. An example of an inﬁnite-dimensional linear space is the space of continuous functions deﬁned on the real line. Consider the space S = {f (x), x ∈ R} for all continuous functions f (·). Clearly S is a linear space. In order to show that this space is inﬁnite-dimensional, we must demonstrate that it cannot be spanned by any ﬁnite set of elements in S. This can be accomplished by noting that the space S contains the linear subspaces made mup of the class of polynomials of order m; that is, the space Sm = {f (x) = j=0 aj xj } for all constants a0 , . . . , am . Clearly, the space Sm is ﬁnite-dimensional (i.e., spanned by the elements x0 , x1 , . . . , xm ). In fact, this space is exactly an m + 1-dimensional linear space since the elements x0 , . . . , xm are linearly independent. Linear independence follows because xj cannot be written as a linear combination of x0 , . . . , xj−1 for any j = 1, 2, . . .. If it could, then j

x =

j−1

a x for all x ∈ R

=0

for some constants a0 , . . . , aj−1 . If this were the case, then the derivatives of j x of all orders would have to be equal to the corresponding derivatives of j−1 the j-th derivative of xj is equal to j! = 0, whereas the j-th =0 a x . But j−1 derivative of =0 a x is zero, leading to a contradiction and implying that x0 , . . . , xm are linearly independent. Consequently, the space S cannot be spanned by any ﬁnite number, say m elements of S, because, if this were possible, then the space of polynomials of order greater than m could also be spanned by the m elements. But this is impossible since such spaces of polynomials have dimension greater than m. Hence, S is inﬁnite-dimensional. From the arguments above, we can easily show that the space of arbitrary densities pZ (z) for a continuous random variable Z deﬁned on the closed ﬁnite interval [0, 1] (i.e., the so-called nonparametric model for such a random variable) spans a space that is inﬁnite-dimensional. This follows by noticing that the functions pZj (z) = (j + 1)−1 z j , 0 ≤ z ≤ 1, j = 1, . . . are densities that are linearly independent.

1.2 Examples of Semiparametric Models Example 1: Restricted Moment Models A common statistical problem is to model the relationship of a response variable Y (possibly vector-valued) as a function of a vector of covariates X. Throughout, we will use the convention that a vector of random variables Z that is not indexed will correspond to a single observation, whereas Zi , i = 1, . . . , n will denote a sample of n iid random vectors. Consider a

4

1 Introduction to Semiparametric Models

family of probability distributions for Z = (Y, X) that satisfy the regression relationship E(Y |X) = µ(X, β), where µ(X, β) is a known function of X and the unknown q-dimensional parameter β. The function µ(X, β) may be linear or nonlinear in β, and it is assumed that β is ﬁnite-dimensional. For example, we might consider a linear model where µ(X, β) = β T X or a nonlinear model, such as a log-linear model, where µ(X, β) = exp(β T X). No other assumptions will be made on the class of probability distributions other than the constraint given by the conditional expectation of Y given X stated above. As we will demonstrate shortly, such models are semiparametric, as they will be deﬁned through an inﬁnite-dimensional parameter space. We will refer to such semiparametric models as “restricted moment models.” These models were studied by Chamberlain (1987) and Newey (1988) in the econometrics literature. They were also popularized in the statistics literature by Liang and Zeger (1986). For illustration, we will take Y to be a one-dimensional random variable that is continuous on the real line. This model can also be written as Y = µ(X, β) + ε, where E(ε|X) = 0. The data are realizations of (Y1 , X1 ), . . . , (Yn , Xn ) that are iid with density for a single observation given by pY,X {y, x; β, η(·)}, where η(·) denotes the inﬁnite-dimensional nuisance parameter function characterizing the joint distribution of ε and X, to be deﬁned shortly. Knowledge of β and the joint distribution of (ε, X) will induce the joint distribution of (Y, X). Since ε = Y − µ(X, β), pY,X (y, x) = pε,X {y − µ(x, β), x}. The restricted moment model only makes the assumption that E(ε|X) = 0. That is, we will allow any joint density pε,X (ε, x) = pε|X (ε|x)pX (x) such that

pε|X (ε|x) 0 for all ε, x, pε|X (ε|x)dε = 1 for all x,

εpε|X (ε|x)dε = 0 for all x, pX (x) ≥ 0 for all x, pX (x)dνX (x) = 1.

1.2 Examples of Semiparametric Models

5

Remark 1. When we refer to the density, joint density, or conditional density of one or more random variables, to avoid confusion, we will often index the variables being used as part of the notation. For example, pY,X (y, x) is the joint density of the random variables Y and X evaluated at the values (y, x). This notation will be suppressed when the variables are obvious. Remark 2. We will use the convention that random variables are denoted by capital letters such as Y and X, whereas realizations of those random variables will be denoted by lowercase letters such as y and x. One exception to this is that the random variable corresponding to the error term Y − µ(X, β) is denoted by the Greek lowercase ε. This is in keeping with the usual notation for such error terms used in statistics. The realization of this error term will also be denoted by the Greek lowercase ε. The distinction between the random variable and the realization of the error term will have to be made in the context it is used and should be obvious in most cases. For example, when we refer to pε,X (ε, x), the subscript ε is a random variable and the argument ε inside the parentheses is the realization. Remark 3. νX (x) is a dominating measure for which densities for the random vector X are deﬁned. For the most part, we will consider ν(·) to be the Lebesgue measure for continuous random variables and the counting measure for discrete random variables. The random variable Y and hence ε will be taken to be continuous random variables dominated by Lebesgue measure dy or dε, respectively. Without going into the measure-theoretical technical details, the class of conditional densities for ε given X, such that E(ε|X) = 0, can be constructed through the following steps. (a) Choose any arbitrary positive function of ε and x (subject to regularity conditions): h(0) (ε, x) > 0. (b) Normalize this function to be a conditional density: h(1) (ε, x) =

h(0) (ε, x) ; h(0) (ε, x)dε

i.e.,

h(1) (ε, x)dε = 1 for all x.

(c) Center it: A random variable ε∗ whose conditional density, given X = x is h(1) (ε , x) = p(ε∗ = ε |X = x), has mean

6

1 Introduction to Semiparametric Models

µ(x) =

ε h(1) (ε , x)dε .

In order to construct a random variable ε whose conditional density, given X = x, has mean zero, we consider ε = ε∗ − µ(X) or ε∗ = ε + µ(X). It is clear that E(ε|X = x) = E(ε∗ |X = x) − µ(x) = 0. Since the transformation from ε to ε∗ , given X = x, has Jacobian equal to 1, the conditional density of ε given X, deﬁned by η1 (ε, x), is given by (1) (1) ε + εh (ε, x)dε, x , η1 (ε, x) = h which, by construction, satisﬁes

εη1 (ε, x)dε = 0 for all x.

Thus, any function η1 (ε, x) constructed as above will satisfy η1 (ε, x) > 0, η1 (ε, x)dε = 1 for all x, εη1 (ε, x)dε = 0 for all x. Since the class of all such conditional densities η1 (ε, x) was derived from arbitrary positive functions h(0) (ε, x) (subject to regularity conditions), and since the space of positive functions is inﬁnite-dimensional, then the set of such resulting conditional densities is also inﬁnite-dimensional. Similarly, we can construct densities for X where pX (x) = η2 (x) such that

η2 (x) > 0, η2 (x)dνX (x) = 1.

The set of all such functions η2 (x) will also be inﬁnite-dimensional as long as the support of X is inﬁnite. Therefore, the restricted moment model is characterized by {β, η1 (ε, x), η2 (x)}, where β ∈ Rq is ﬁnite-dimensional and η1 (ε, x) = pε|X (ε|x), η2 (x) = pX (x) are inﬁnite-dimensional. Consequently, the joint density of (Y, X) is given by pY,X {y, x; β, η1 (·), η2 (·)} = pY |X {y|x; β, η1 (·)}pX {x; η2 (·)} = η1 {y − µ(x, β), x}η2 (x). This is an example of a semiparametric model because the parametrization is through a ﬁnite-dimensional parameter of interest β ∈ Rq and inﬁnitedimensional nuisance parameters {η1 (·), η2 (·)}. Contrast this semiparametric model with the more traditional parametric model where

1.2 Examples of Semiparametric Models

7

Yi = µ(Xi , β) + εi , i = 1, . . . , n, 2

where εi are iid N (0, σ ). That is, pY |X (y|x; β, σ 2 ) =

1 {y − µ(x, β)}2 . exp − 1 2 σ2 (2πσ 2 ) 2 1

This model is much more restrictive than the semiparametric model deﬁned earlier. Example 2: Proportional Hazards Model In many biomedical applications, we are often interested in modeling the survival time of individuals as functions of covariates. Let the response variable be the survival time of an individual, denoted by T , whose distribution depends on explanatory variables X. A popular model in survival analysis is Cox’s proportional hazards model, which was ﬁrst introduced in the seminal paper by Cox (1972). This model assumes that the conditional hazard rate, as a function of X, is given by P (t ≤ T < t + h|T t, X) λ(t|X) = lim h→0 h = λ(t) exp(β T X). The proportional hazards model is especially convenient when survival times may be right censored, as we will discuss in greater detail in Chapter 5. Interest often focuses on the ﬁnite-dimensional parameter β, as this describes the magnitude of the eﬀect that the covariates have on the survival time. The underlying hazard function λ(t) is left unspeciﬁed and is considered a nuisance parameter. Since this function can be any positive function in t, subject to some regularity conditions, it, too, is inﬁnite-dimensional. Using the fact that the density of a positive random variable is related to the hazard function through ⎧ ⎫ ⎨ t ⎬ pT (t) = λ(t) exp − λ(u)du , ⎩ ⎭ 0

then the density of a single observation Z = (T, X) is given by pT,X {t, x; β, λ(·), η2 (·)} = pT |X {t|x; β, λ(·)}η2 (x), where

⎧ ⎫ t ⎨ ⎬ pT |X {t|x; β, λ(·)} = λ(t) exp(β T x) exp − exp(β T x) λ(u)du , ⎩ ⎭ 0

and exactly as in Example 1, η2 (x) is deﬁned as a function of x such that

8

1 Introduction to Semiparametric Models

η2 (x) 0, η2 (x)dνX (x) = 1,

for all x. The proportional hazards model has gained a great deal of popularity because it is more ﬂexible than a ﬁnite-dimensional parametric model, that assumes that the hazard function for T has a speciﬁc functional form in terms of a few parameters; e.g., λ(t, η) =η (constant hazard over time – exponential model), or λ(t, η) =η1 tη2 (Weibull model).

Example 3: Nonparametric Model In the two previous examples, the probability models were written in terms of an inﬁnite-dimensional parameter θ, which was partitioned as {β T , η(·)}, where β was the ﬁnite-dimensional parameter of interest and η(·) was the inﬁnite-dimensional nuisance parameter. We now consider the problem of estimating the moments of a single random variable Z where we put no restriction on the distribution of Z except that the moments of interest exist. That is, we denote the density of Z by θ(z), where θ(z) can be any posi tive function of z such that θ(z)dνZ (z) = 1 and any additional restrictions necessary for the moments of interest to exist. Clearly, the class of all θ(·) is inﬁnite-dimensional as long as the support of Z is inﬁnite. Suppose we were interested in estimating some functional of θ(·), say β(θ) (forexample, the ﬁrst or second moment E(Z) or E(Z 2 ), where β(θ) is equal to zθ(z)dνZ (z) or z 2 θ(z)dνZ (z), respectively). For such a problem, it is not convenient to try to partition the parameter space in terms of the parameter β of interest and a nuisance parameter but rather to work directly with the functional β(θ).

1.3 Semiparametric Estimators In a semiparametric model, a semiparametric estimator for β, say βˆn , is one that, loosely speaking, has the property that it is consistent and asymptotically normal in the sense that P {β,η(·)}

(βˆn − β) −−−−−−→ 0, D{β,η(·)} n1/2 (βˆn − β) −−−−−−→ N (0, Σq×q {β, η(·)}),

1.3 Semiparametric Estimators

9

for all densities “p{z, β, η(·)}” within some semiparametric family, where P {β,η(·)}

D{β,η(·)}

−−−−−−→ denotes convergence in probability and −−−−−−→ denotes convergence in distribution when the density of the random variable Z is p{z, β, η(·)}. We know, for example, that the solution to the linear estimating equations n

Aq×1 (Xi , βˆn ) Yi − µ(Xi , βˆn ) = 0q×1 ,

i=1

under suitable regularity conditions, leads to an estimator for β that is consistent and asymptotically normal for the semiparametric restricted moment model of Example 1. In fact, this is the basis for “generalized estimating equations” (GEE) proposed by Liang and Zeger (1986). The maximum partial likelihood estimator proposed by Cox (1972, 1975) is an example of a semiparametric estimator for β in the proportional hazards model given in Example 2. Also, in Example 3, a semiparametric estimator for the ﬁrst and second moments is given by n−1 Zi and n−1 Zi2 , respectively. Some issues that arise when studying semiparametric models are: (i) How do we ﬁnd semiparametric estimators, or do they even exist? (ii) How do we ﬁnd the best estimator among the class of semiparametric estimators? Both of these problems are diﬃcult. Understanding the geometry of estimators, more speciﬁcally the geometry of the inﬂuence function of estimators, will help us in this regard. Much of this book will rely heavily on geometric constructions. We will deﬁne the inﬂuence function of an asymptotically linear estimator and describe the geometry of all possible inﬂuence functions for a statistical model. We will start by looking at ﬁnite-dimensional parametric models and then generalize the results to the more complicated inﬁnite-dimensional semiparametric models. Since the geometry that is considered is the geometry of Hilbert spaces, we begin with a quick review of Hilbert spaces, the notion of orthogonality, minimum distance, and how this relates to eﬃcient estimators (i.e., estimators with the smallest asymptotic variance).

2 Hilbert Space for Random Vectors

In this section, we will introduce a Hilbert space without going into much of the technical details. We will focus primarily on the Hilbert space whose elements are random vectors with mean zero and ﬁnite variance that will be used throughout the book. For more details about Hilbert spaces, we recommend that the reader study Chapter 3 of Luenberger (1969).

2.1 The Space of Mean-Zero q-dimensional Random Functions As stated earlier, data are envisioned as realizations of the random vectors Z1 , Z2 , . . ., Zn , assumed iid. Let Z denote the random vector for a single observation. As always, there is an underlying probability space (Z , A, P ), where Z denotes the sample space, A the corresponding σ-algebra, and P the probability measure. For the time being, we will not consider a statistical model consisting of a family of probability measures, but rather we will assume that P is the true probability measure that generates the realizations of Z. Consider the space consisting of q-dimensional mean-zero random functions of Z, h : Z → Rq , where h(Z) is measurable and also satisﬁes (i) E{h(Z)} = 0, (ii) E{hT (Z)h(Z)} < ∞. Since the elements of this space are random functions, when we refer to an element as h, we implicitly mean h(Z). Clearly, the space of all such h that satisfy (i) and (ii) is a linear space. By linear, we mean that if h1 , h2 are elements of the space, then for any real constants a and b, ah1 + bh2 also belongs to the space.

12

2 Hilbert Space for Random Vectors

In the same way that we consider points in Euclidean space as vectors from the origin, here we will consider the q-dimensional random functions as points in a space. The intuition we have developed in understanding the geometry of two- and three-dimensional Euclidean space will aid us in understanding the geometry of more complex spaces through analogy. The random function h(Z) = 0q×1 will denote the origin of this space. The Dimension of the Space of Mean-Zero Random Functions An element of the linear space deﬁned above is a q-dimensional function of Z. This should not be confused with the dimensionality of the space itself. To illustrate this point more clearly, let us ﬁrst consider the space of onedimensional random functions of Z (random variables), where Z is a discrete variable with ﬁnite support. Speciﬁcally, let Z be allowed to take on one of a ﬁnite k number of values z1 , . . . , zk with positive probabilities π1 , . . . , πk , where i=1 πi = 1. For such a case, any one-dimensional random function of Z can be deﬁned as h(Z) = a1 I(Z = z1 ) + . . . + ak I(Z = zk ) for any real valued constants a1 , . . . , ak , where I(·) denotes the indicator function. The space of all such random functions is a linear space spanned by the k linearly independent functions I(Z = zi ), i = 1, . . . , k. Hence this space is a k-dimensional linear space. If we put the further constraint that the mean k must be zero (i.e., E{h(Z)} = 0), then this implies that i=1 ai πi = 0, k−1 or equivalently that ak = −( i=1 ai πi )/πk . Some simple algebra leads us to conclude that the space of one-dimensional mean-zero random functions of Z is a linear space spanned by the k − 1 linearly independent functions {I(Z = zi ) − ππki I(Z = zk )}, i = 1, . . . , k − 1. Hence this space is a k − 1dimensional linear space. Similarly, the space of q-dimensional mean-zero random functions of Z, where Z has ﬁnite support at the k values z1 , . . . , zk , can be shown to be a linear space with dimension q × (k − 1). Clearly, as the number of support points k for the distribution of Z increases, so does the dimension of the linear space of q-dimensional mean-zero random functions of Z. If the support of the random vector Z is inﬁnite, as would be the case if any element of the random vector Z was a continuous random variable, then the space of measurable functions that make up the Hilbert space will be inﬁnite-dimensional. As we indicated in Section 1.1, the set of one-dimensional continuous functions of Z is inﬁnite-dimensional. Consequently, the set of qdimensional continuous functions will also be inﬁnite-dimensional. Clearly, the set of q-dimensional measurable functions is a larger class and hence must also be inﬁnite-dimensional.

2.2 Hilbert Space

13

2.2 Hilbert Space A Hilbert space, denoted by H, is a complete normed linear vector space equipped with an inner product. As well as being a linear space, a Hilbert space also allows us to consider distance between elements and angles and orthogonality between vectors in the space. This is accomplished by deﬁning an inner product. Deﬁnition 1. Corresponding to each pair of elements h1 , h2 belonging to a linear vector space H, an inner product, deﬁned by h1 , h2 , is a function that maps to the real line. That is, h1 , h2 is a scalar that satisﬁes 1. h1 , h2 = h2 , h1 , 2. h1 + h2 , h3 = h1 , h3 + h2 , h3 , where h1 , h2 , h3 belong to H, 3. λh1 , h2 = λ h1 , h2 for any scalar constant λ, 4. h1 , h1 ≥ 0 with equality if and only if h1 = 0. Note 1. In some cases, the function ·, · may satisfy conditions 1–3 above and the ﬁrst part of condition 4, but h1 , h1 = 0 may not imply that h1 = 0. In that case, we can still deﬁne a Hilbert space by identifying equivalence classes where individual elements in our space correspond to diﬀerent equivalence classes. Deﬁnition 2. For the linear vector space of q-dimensional measurable random functions with mean zero and ﬁnite second moments, we can deﬁne the inner product

h1 , h2 by E(hT1 h2 ). We shall refer to this inner product as the “covariance inner product.” This deﬁnition of inner product clearly satisﬁes the ﬁrst three conditions of the deﬁnition given above. As for condition 4, we can deﬁne an equivalence class where h1 is equivalent to h2 , h1 ≡ h 2 , if h1 = h2 a.e. or P (h1 = h2 ) = 0. In this book, we will generally not concern ourselves with such measure-theoretical subtleties. Once an inner product is deﬁned, we then deﬁne the norm or “length” of any vector (i.e., element of H ) (distance from any point h ∈ H to the origin) as

h = h, h1/2 . Hilbert spaces also allow us to deﬁne orthogonality; that is, h1 , h2 ∈ H are orthogonal if h1 , h2 = 0.

14

2 Hilbert Space for Random Vectors

Remark 1. Technically speaking, the deﬁnitions above are those for a preHilbert space. In order to be a Hilbert space, we also need the space to be complete (i.e., every Cauchy sequence has a limit point that belongs to the space). That the space of q-dimensional random functions with mean zero and bounded second moments is complete follows from the L2 -completeness theorem (see Lo`eve 1963, p. 161) and hence is a Hilbert space.

2.3 Linear Subspace of a Hilbert Space and the Projection Theorem A space U ⊂ H is a linear subspace if u1 , u2 ∈ U implies that au1 + bu2 ∈ U for all scalar constants a, b. A linear subspace must contain the origin. This is clear by letting the scalars be a = b = 0. A simple example of a linear subspace is obtained by taking h1 , . . . , hk to be arbitrary elements of a Hilbert space. Then the space a1 h1 + · · · + ak hk for all scalars (a1 , . . . , ak ) ∈ Rk is a linear subspace spanned by {h1 , . . . , hk }. One of the key results for Hilbert spaces, which we will use repeatedly throughout this book, is given by the projection theorem. Projection Theorem for Hilbert Spaces Theorem 2.1. Let H be a Hilbert space and U a linear subspace that is closed (i.e., contains all its limit points). Corresponding to any h ∈ H, there exists a unique u0 ∈ U that is closest to h; that is,

h − u0 ≤ h − u for all u ∈ U. Furthermore, h − u0 is orthogonal to U; that is,

h − u0 , u = 0 for all u ∈ U. We refer to u0 as the projection of h onto the space U, and this is denoted as Π(h|U). Moreover, u0 is the only element u ∈ U such that h − u is orthogonal to U (see Figure 2.1). The proof of the projection theorem for arbitrary Hilbert spaces is not much diﬀerent or more diﬃcult than for a ﬁnite-dimensional Euclidean space. The condition that a Hilbert space be complete is necessary to guarantee the existence of the projection. A formal proof can be found in Luenberger (1969, Theorem 2, p. 51). The intuition of orthogonality and distance carries over very nicely from simple Euclidean spaces to more complex Hilbert spaces. A simple consequence of orthogonality is the Pythagorean theorem, which we state for completeness. Theorem 2.2. Pythagorean Theorem If h1 and h2 are orthogonal elements of the Hilbert space H (i.e., h1 , h2 = 0), then

h1 + h2 2 = h1 2 + h2 2 .

2.4 Some Simple Examples of the Application of the Projection Theorem

15

Geometrically

(If we shift this to the origin we get (h – uo))

h (h – uo)

linear subspace ϑ

uo

origin

Fig. 2.1. Projection onto a linear subspace

2.4 Some Simple Examples of the Application of the Projection Theorem Example 1: One-Dimensional Random Functions Consider the Hilbert space H of one-dimensional random functions, h(Z), with mean zero and ﬁnite variance equipped with the inner product

h1 , h2 = E(h1 h2 ) for h1 (Z), h2 (Z) ∈ H. Let u1 (Z), . . . , uk (Z) be arbitrary elements of this space and U be the linear subspace spanned by {u1 , · · · , uk }. That is, U = {aT u;

for a ∈ Rk },

where ⎞ u1 ⎜ ⎟ = ⎝ ... ⎠ . ⎛

uk×1

uk The space U is an example of a ﬁnite-dimensional linear subspace since it is spanned by the ﬁnite number of elements u1 (Z), . . . , uk (Z). This subspace is contained in the inﬁnite-dimensional Hilbert space H. Moreover, if the elements u1 (Z), . . . , uk (Z) are linearly independent, then the dimension of U is identically equal to k. Let h be an arbitrary element of H. Then the projection of h onto the linear subspace U is given by the unique element aT0 u that satisﬁes

h − aT0 u, aT u = 0 for all a = (a1 , . . . , ak )T ∈ Rk ,

16

2 Hilbert Space for Random Vectors

or k

aj h − aT0 u, uj = 0 for all aj , j = 1, . . . , k.

j=1

Equivalently, h − aT0 u, uj = 0 for all j = 1, . . . , k, or E{(h − aT0 u)uT } = 0(1×k) , or E(huT ) − aT0 E(uuT ) = 0(1×k) . Any solution of a0 such that aT0 E(uuT ) = E(huT ) would lead to the unique projection aT0 u. If E(uuT ) is positive deﬁnite, and therefore has a unique inverse, then aT0 = E(huT ){E(uuT )}−1 , in which case the unique projection will be u0 = aT0 u = E(huT ){E(uuT )}−1 u. The norm-squared of this projection is equal to E(huT ){E(uuT )}−1 E(uh). By the Pythagorean theorem,

h − aT0 u 2 = E(h − aT0 u)2 = E(h2 ) − E(huT ){E(uuT )}−1 E(uh).

Example 2: q-dimensional Random Functions Let H be the Hilbert space of mean-zero q-dimensional measurable random functions with ﬁnite second moments equipped with the inner product

h1 , h2 = E(hT1 h2 ).

2.4 Some Simple Examples of the Application of the Projection Theorem

17

[E(h2) – E(hu){E(V V )}–1E(V h)]1/2 {E(h2)}1/2 h

ϑ

[E(hV ){E(V V )}–1E(V h)]1/2 Fig. 2.2. Geometric illustration of the Pythagorean theorem

Let “v(Z)” be an r-dimensional random function with mean zero and E(v T v) < ∞. Consider the linear subspace U spanned by v(Z); that is, U = {B q×r v, where B is any arbitrary q × r matrix of real numbers}. The linear subspace U deﬁned above is a ﬁnite-dimensional linear subspace contained in the inﬁnite-dimensional Hilbert space H. If the elements v1 (Z), . . . , vr (Z) are linearly independent, then the dimension of U is q × r. This can easily be seen by noting that U is spanned by the q × r linearly independent elements uij (Z), i = 1, . . . , q, j = 1, . . . , r, of H, where, for any i = 1, . . . , q, we take the element uq×1 ij (Z) ∈ H to be the q-dimensional function of Z, where all except the i-th element are equal to 0 and the i-th element is equal to vj (Z) for j = 1, . . . , r. We now consider the problem of ﬁnding the projection of an arbitrary element h ∈ H onto U. By the projection theorem, such a projection B0 v is unique and must satisfy E{(h − B0 v)T Bv} = 0 for all B ∈ Rq×r .

(2.1)

The statement above being true for all B is equivalent to E{(h − B0 v)v T } = 0q×r

(matrix of all zeros).

(2.2)

To establish (2.2), we write E{(h − B0 v)T Bv} =

i

Bij E{(h − B0 v)i vj },

(2.3)

j

where (h − B0 v)i denotes the i-th element of the q-dimensional vector (h − B0 v), vj denotes the j-th element of the r-dimensional vector v, and Bij denotes the (i, j)-th element of the matrix B. If we take Bij = 1 for i = i and j = j , and 0 otherwise, it becomes clear from (2.3) that

18

2 Hilbert Space for Random Vectors

E{(h − B0 v)i vj } = 0 for all i , j and hence E{(h − B0 v)v T } = 0q×r . Conversely, if (2.2) is true, then for any matrix B we have (2.1) being true. Consequently, by (2.2), we deduce that E(hv T ) = B0 E(vv T ). Therefore, assuming E(vv T ) is nonsingular (i.e., positive deﬁnite), B0 = E(hv T ){E(vv T )}−1 . Hence, the unique projection is Π(h|U) = E(hv T ){E(vv T )}−1 v.

(2.4)

Remark 2. Finding the projection of the q-dimensional vector h onto the subspace U is equivalent to taking each element of h and projecting it individually to the subspace spanned by {v1 , . . . , vr } for the Hilbert space of onedimensional random functions considered in Example 1 and then stacking these individual projections into a vector. This can be deduced by noting that minimizing h − Bv 2 corresponds to minimizing E{(h − Bv)T (h − Bv)} =

q i=1

=

q i=1

E(h − Bv)2i ⎛ E ⎝hi −

r

⎞2 Bij vj ⎠ .

(2.5)

j=1

Since the Bij are arbitrary, we can minimize the sum in (2.5) by minimizing each of the elements in the sum separately. The norm-squared of this projection is given by E v T {E(vv T )}−1 E(vhT )E(hv T ){E(vv T )}−1 v , and, by the Pythagorean theorem (see Figure 2.2 for illustration), the normsquared of the residual (h − B0 v) is E(hT h) − E v T {E(vv T )}−1 E(vhT )E(hv T ){E(vv T )}−1 v = tr{[hhT ] − E(hv T ){E(vv T )}−1 E(vhT )}. There are other properties of Hilbert spaces that will be used throughout the book. Rather than giving all the properties in this introductory chapter, we will instead deﬁne these as they are needed. There is, however, one very important result that we do wish to highlight. This is the Cauchy-Schwartz inequality given in Theorem 2.3.

2.5 Exercises for Chapter 2

19

Theorem 2.3. Cauchy-Schwartz inequality For any two elements h1 , h2 ∈ H, | h1 , h2 |2 ≤ h1 2 h2 2 , with equality holding if and only if h1 and h2 are linearly related; i.e., h1 = ch2 for some scalar constant c.

2.5 Exercises for Chapter 2 1. Prove the projection theorem (Theorem 2.1) for Hilbert spaces. 2. Let Z = (Z1 , . . . , Zp )T be a p-dimensional multivariate normally distributed random vector with mean zero and covariance matrix Σp×p . We also write Z as the partitioned vector Z = (Y1T , Y2T )T , where Y1q×1 = (p−q)×1 (Z1 , . . . , Zq )T and Y2 = (Zq+1 , . . . , Zp )T , q < p, and ⎞ ⎛ q×(p−q) Σ12 Σq×q 11 ⎠, where Σ=⎝ (p−q)×q (p−q)×(p−q) Σ21 Σ22 Σ11 = E(Y1 Y1T ), Σ12 = E(Y1 Y2T ), Σ21 = E(Y2 Y1T ), Σ22 = E(Y2 Y2T ). Let H be the Hilbert space of all q-dimensional measurable functions of Z with mean zero, ﬁnite variance, and equipped with the covariance inner product. Let U be the linear subspace spanned by Y2 ; i.e., U consists of all the elements B q×(p−q) Y2 : for all q × (p − q) matrices B . (a) Find the projection of Y1 onto U. (b) Compute the norm of the residual

Y1 − Π(Y1 |U) . 3. Let Z = (Z1 , Z2 )T be a bivariate distributed vector with mean 2 normally σ1 σ12 zero and covariance matrix . σ12 σ22 Consider the Hilbert space of all one-dimensional measurable functions of Z with mean zero, ﬁnite variance, and covariance inner product. Let U denote the linear subspace spanned by Z2 and (Z12 − σ12 ); i.e., the space whose elements are a1 (Z12 − σ12 ) + a2 Z2

for all a1 , a2 .

(a) Find the projection of Z1 onto the space U. (b) Find the variance of the residual (i.e., var{Z1 − Π(Z1 |U)}).

3 The Geometry of Inf luence Functions

As we will describe shortly, most reasonable estimators for the parameter β, in either parametric or semiparametric models, are asymptotically linear and can be uniquely characterized by the inﬂuence function of the estimator. The class of inﬂuence functions for such estimators belongs to the Hilbert space of all mean-zero q-dimensional random functions with ﬁnite variance that was deﬁned in Chapter 2. As such, this construction will allow us to view estimators or, more speciﬁcally, the inﬂuence function of estimators, from a geometric point of view. This will give us intuitive insight into the construction of such estimators and a geometric way of assessing the relative eﬃciencies of the various estimators. As always, consider the statistical model where Z1 , . . . , Zn are iid random vectors and the density of a single Z is assumed to belong to the class {pZ (z; θ), θ Ω} with respect to some dominating measure νZ . The parameter θ can be written as (β T , η T )T , where β q×1 is the parameter of interest and η, the nuisance parameter, may be ﬁnite- or inﬁnite-dimensional. The truth will be denoted by θ0 = (β0T , η0T )T. For the remainder of this chapter, we will only consider parametric models where θ = (β T , η T )T and the vector θ is p-dimensional, the parameter of interest β is q-dimensional, and the nuisance parameter η is r-dimensional, with p = q + r. An estimator βˆn of β is a q-dimensional measurable random function of Z1 , . . . , Zn . Most reasonable estimators for β are asymptotically linear; that is, there exists a random vector (i.e., a q-dimensional measurable random function) ϕq×1 (Z), such that E{ϕ(Z)} = 0q×1 , n1/2 (βˆn − β0 ) = n−1/2

n

ϕ(Zi ) + op (1),

(3.1)

i=1

where op (1) is a term that converges in probability to zero as n goes to inﬁnity and E(ϕϕT ) is ﬁnite and nonsingular. Remark 1. The function ϕ(Z) is deﬁned with respect to the true distribution p(z, θ0 ) that generates the data. Consequently, we sometimes may write

22

3 The Geometry of Influence Functions

ϕ(Z, θ) to emphasize that this random function will vary according to the value of θ in the model. Unless otherwise stated, it will be assumed that ϕ(Z) is evaluated at the truth and expectations are taken with respect to the truth. Therefore, E{ϕ(Z)} is shorthand for Eθ0 {ϕ(Z, θ0 )}. The random vector ϕ(Zi ) in (3.1) is referred to as the i-th inﬂuence function of the estimator βˆn or the inﬂuence function of the i-th observation of the estimator βˆn . The term inﬂuence function comes from the robustness literature, where, to ﬁrst order, ϕ(Zi ) is the inﬂuence of the i-th observation on βˆn ; see Hampel (1974). Example 1. As a simple example, consider the model where Z1 , . . . , Zn are iid N (µ, σ 2 ). The maximum likelihood µ and σ 2 are given by nestimators for n −1 2 −1 2 µ ˆn = n ˆn = n ˆn ) , respectively. That the i=1 Zi and σ i=1 (Zi − µ estimator µ ˆn for µ is asymptotically linear follows immediately because n1/2 (ˆ µn − µ0 ) = n−1/2

n

(Zi − µ0 ).

i=1

Therefore, µ ˆn is an asymptotically linear estimator for µ whose i-th inﬂuence function is given by ϕ(Zi ) = (Zi − µ0 ). After some straightforward algebra, we can express the estimator σ ˆn2 minus the estimand as (ˆ σn2 − σ02 ) = n−1

n

{(Zi − µ0 )2 − σ02 } + (ˆ µn − µ0 )2 .

(3.2)

i=1

Multiplying (3.2) by n1/2 , we obtain σn2 − σ02 ) = n−1/2 n1/2 (ˆ

n

{(Zi − µ0 )2 − σ02 } + n1/2 (ˆ µn − µ0 )2 .

i=1

Since n1/2 (ˆ µn −µ0 ) converges to a normal distribution and (ˆ µn −µ0 ) converges µn −µ0 )2 converges in probability in probability to zero, this implies that n1/2 (ˆ to zero (i.e., is op (1)). Consequently, we have demonstrated that σ ˆn2 is an 2 asymptotically linear estimator for σ whose i-th inﬂuence function is given by ϕ(Zi ) = {(Zi − µ0 )2 − σ02 }. When considering the asymptotic properties of an asymptotically linear estimator (e.g., asymptotic normality and asymptotic variance), it suﬃces to consider the inﬂuence function of the estimator. This follows as a simple consequence of the central limit theorem (CLT). Since, by deﬁnition,

3 The Geometry of Influence Functions

n

1/2

(βˆn − β0 ) = n−1/2

n

23

ϕ(Zi ) + op (1),

i=1

then, by the central limit theorem, n

−1/2

n

D

ϕ(Zi ) −→ N 0

q×1

, E(ϕϕ ) , T

i=1

and, by Slutsky’s theorem, D n1/2 (βˆn − β0 ) −→ N 0, E(ϕϕT ) . In an asymptotic sense, an asymptotically linear estimator can be identiﬁed through its inﬂuence function, as we now demonstrate in the following theorem. Theorem 3.1. An asymptotically linear estimator has a unique (a.s.) inﬂuence function. Proof. By contradiction Suppose not. Then there exists another inﬂuence function ϕ∗ (Z) such that E{ϕ∗ (Z)} = 0, and n1/2 (βˆn − β0 ) = n−1/2

n

ϕ∗ (Zi ) + op (1).

i=1

Since n1/2 (βˆn − β0 ) is also equal to n−1/2

n

ϕ(Zi ) + op (1), this implies that

i=1

n−1/2

n

{ϕ(Zi ) − ϕ∗ (Zi )} = op (1).

i=1

However, by the CLT, n

−1/2

n

∗

D

∗

∗ T

{ϕ(Zi ) − ϕ (Zi )} −→ N 0, E{(ϕ − ϕ )(ϕ − ϕ ) } .

i=1

In order for this limiting normal distribution to be op (1), we would require that the covariance matrix E{(ϕ − ϕ∗ )(ϕ − ϕ∗ )T } = 0q×q , which implies that ϕ(Z) = ϕ∗ (Z) a.s.

24

3 The Geometry of Influence Functions

The representation of estimators through their inﬂuence function lends itself nicely to geometric interpretations in terms of Hilbert spaces, discussed in Chapter 2. Before describing this geometry, we brieﬂy comment on some regularity conditions that will be imposed on the class of estimators we will consider. Reminder. We know that the variance of any unbiased estimator must be greater than or equal to the Cr` amer-Rao lower bound; see, for example, Casella and Berger (2002, Section 7.3). When considering asymptotic theory, where we let the sample size n go to inﬁnity, most reasonable estimators are asymptotically unbiased. Thus, we might expect the asymptotic variance of such asymptotically unbiased estimators also to be greater than the Cr` amerRao lower bound. This indeed is the case for the most part, and estimators whose asymptotic variance equals the Cr` amer-Rao lower bound are referred to as asymptotically eﬃcient. For parametric models, with suitable regularity conditions, the maximum likelihood estimator (MLE) is an example of an eﬃcient estimator. One of the peculiarities of asymptotic theory is that asymptotically unbiased estimators can be constructed that have asymptotic variance equal to the Cr` amer-Rao lower bound for most of the parameter values in the model but have smaller variance than the Cr` amer-Rao lower bound for the other parameters. Such estimators are referred to as super-eﬃcient and for completeness we give the construction of such an estimator (Hodges) as an example.

3.1 Super-Eﬃciency Example Due to Hodges Let Z1 , . . . , Zn be iid N (µ, 1), µ ∈ R. For this simple model, we know that the maximum n likelihood estimator (MLE) of µ is given by the sample mean Z¯n = n−1 i=1 Zi and that D(µ)

n1/2 (Z¯n − µ) −−−→ N (0, 1). Now, consider the estimator µ ˆn given by Hodges in 1951 (see LeCam, 1953): Z¯n if |Z¯n | > n−1/4 µ ˆn = 0 if |Z¯n | ≤ n−1/4 . Some of the properties of this estimator are as follows. If µ = 0, then with increasing probability, the support of Z¯n moves away from 0 (see Figure 3.1).

3.1 Super-Eﬃciency

25

Z –n–1/4 0

n–1/4

µ

Fig. 3.1. When µ = 0, Pµ (Z¯n = µ ˆn ) → 0 D(µ) Therefore n1/2 (Z¯n −µ) = n1/2 (ˆ µn −µ)+op (1) and n1/2 (ˆ µn −µ) −−−→ N (0, 1). If µ = 0, then the support of Z¯n will be concentrated in an O(n−1/2 ) neighborhood about the origin and hence, with increasing probability, will be within ±n−1/4 (see Figure 3.2).

Z

–n–1/4 n–1/4

Fig. 3.2. When µ = 0, P0 {|Z¯n | < n−1/4 } → 1

Therefore, this implies that P0 (ˆ µn = 0) → 1. Hence P0 n1/2 µ ˆn = 0 → 1, and P

D(0)

0 n1/2 (ˆ µn − 0) −→ 0 or −−−→ N (0, 0). Consequently, the asymptotic variance of 1/2 n (ˆ µn − µ) is equal to 1 for all µ = 0, as it is for the MLE Z¯n , but for µ = 0, the asymptotic variance of n1/2 (ˆ µn − µ) equals 0 and thus is super-eﬃcient. Although super-eﬃciency, at the surface, may seem like a good property for an estimator to possess, upon further study we ﬁnd that super-eﬃciency is gained at the expense of poor estimation in a neighborhood of zero. To

26

3 The Geometry of Influence Functions

illustrate this point, consider the sequence µn = n−1/3 , which converges to zero, the value at which the estimator µ ˆn is super-eﬃcient. The MLE has the property that D(µn ) n1/2 (Z¯n − µn ) −−−−→ N (0, 1). However, because Z¯n concentrates its mass in an O(n−1/2 ) neighborhood about µn = n−1/3 , which eventually, as n increases, will be completely contained within the range ±n−1/4 with probability converging to one (see Figure 3.3).

Z

–n–1/4 n–1/3

n–1/4

Fig. 3.3. When µn = n−1/3 , Pµn (ˆ µn = 0) → 1

Therefore,

µn − µn ) = −n1/2 µn → 1. Pµn n1/2 (ˆ

Consequently, if µn = n−1/3 , then −n1/2 µn → −∞. Therefore, n1/2 (ˆ µn − µn ) diverges to −∞. Although super-eﬃcient estimators exist, they are unnatural and have undesirable local properties associated with them. Therefore, in order to avoid problems associated with super-eﬃcient estimators, we will impose some additional regularity conditions on the class of estimators that will exclude such estimators. Speciﬁcally, we will require that an estimator be regular, as we now deﬁne. Deﬁnition 1. Consider a local data generating process (LDGP), where, for each n, the data are distributed according to “θn ,” where n1/2 (θn − θ∗ ) converges to a constant (i.e., θn is close to some ﬁxed parameter θ∗ ). That is, Z1n , Z2n , . . . , Znn

are iid p(z, θn ),

3.1 Super-Eﬃciency

where θn = (βnT , ηnT )T ,

T

27

T

θ∗ = (β ∗ , η ∗ )T .

An estimator βˆn , more speciﬁcally βˆn (Z1n , . . . , Znn ), is said to be regular if, for each θ∗ , n1/2 (βˆn − βn ) has a limiting distribution that does not depend on the LDGP. For our purposes, this will ordinarily mean that if D(θ∗ ) n1/2 βˆn (Z1n , . . . , Znn ) − β ∗ −−−−→ N (0, Σ∗ ) , where Z1n , . . . , Znn then

are iid p(z, θ∗ ),

for all n,

D(θ ) n n1/2 βˆn (Z1n , . . . , Znn ) − βn −−−−→ N (0, Σ∗ ),

where Z1n , . . . , Znn 1/2

∗

are iid p(z, θn ),

and n (θn − θ ) → τ , where τ is any arbitrary constant vector. It is easy to see that, in our previous example, the MLE Z¯n is a regular estimator, whereas the super-eﬃcient estimator µ ˆ n , given by Hodges, is not. From now on, we will restrict ourselves to regular estimators; in fact, we will only consider estimators that are regular and asymptotically linear (RAL). Although most reasonable estimators are RAL, regular estimators do exist that are not asymptotically linear. However, as a consequence of H´ ajek’s (1970) representation theorem, it can be shown that the most eﬃcient regular estimator is asymptotically linear; hence, it is reasonable to restrict attention to RAL estimators. In Theorem 3.2 and its subsequent corollary, given below, we present a very powerful result that allows us to describe the geometry of inﬂuence functions for regular asymptotically linear (RAL) estimators. This will aid us in deﬁning and visualizing eﬃciency and will also help us generalize ideas to semiparametric models. First, we deﬁne the score vector for a single observation Z in a parametric model, where Z ∼ pZ (z, θ), θ = (β T , η T )T , by Sθ (Z, θ0 ), where p×1

Sθ (z, θ0 ) =

∂ log pZ (z, θ) ∂θ

(3.3) θ=θ0

is the p-dimensional vector of derivatives of the log-likelihood with respect to the elements of the parameter θ and θ0 denotes the true value of θ that generates the data. This vector can be partitioned according to β (the parameters of interest) and η (the nuisance parameters) as

28

3 The Geometry of Influence Functions

Sθ (Z, θ0 ) = {SβT (Z, θ0 ), SηT (Z, θ0 )}T , where Sβ (z, θ0 ) =

∂ log pZ (z, θ) ∂β

q×1

∂ log pZ (z, θ) ∂η

r×1

θ=θ0

and Sη (z, θ0 ) =

. θ=θ0

Although, in many applications, we can naturally partition the parameter space θ as (β T , η T )T , we will ﬁrst give results for the more general representation where we deﬁne the q-dimensional parameter of interest as a smooth q-dimensional function of the p-dimensional parameter θ; namely, β(θ). As we will show later, especially when we consider inﬁnite-dimensional semiparametric models, in some applications this will be a more natural representation. For parametric models, this is really not a great distinction, as we can always reparametrize the problem so that there is a one-to-one relationship between {β T (θ), η T (θ)}T and θ for some r-dimensional nuisance function η(θ). Theorem 3.2. Let the parameter of interest β(θ) be a q-dimensional function of the p-dimensional parameter θ, q < p, such that Γq×p (θ) = ∂β(θ)/∂θT , the q × p-dimensional matrix of partial derivatives, exists, has rank q, and is continuous in θ in a neighborhood of the truth θ0 . Also let βˆn be an asymptotically linear estimator with inﬂuence function ϕ(Z) such that Eθ (ϕT ϕ) exists and is continuous in θ in a neighborhood of θ0 . Then, if βˆn is regular, this will imply that E{ϕ(Z)SθT (Z, θ0 )} = Γ(θ0 ). (3.4) In the special case where θ can be partitioned as (β T , η T )T , we obtain the following corollary. Corollary 1. (i) E{ϕ(Z)SβT (Z, θ0 )} = I q×q and (ii) E{ϕ(Z)SηT (Z, θ0 )} = 0q×r , where I q×q denotes the q ×q identity matrix and 0q×r denotes the q ×r matrix of zeros.

3.2 m-Estimators (Quick Review)

29

Theorem 3.2 follows from the deﬁnition of regularity together with suﬃcient smoothness conditions that makes a local data generating process contiguous (to be deﬁned shortly) to the sequence of distributions at the truth. For completeness, we will give an outline of the proof. Before giving the general proof of Theorem 3.2, which is complicated and can be skipped by the reader not interested in all the technical details, we can gain some insight by ﬁrst showing how Corollary 1 could be proved for the special (and important) case of the class of m-estimators.

3.2 m-Estimators (Quick Review) In order to deﬁne an m-estimator, we consider a p × 1-dimensional function of Z and θ, m(Z, θ), such that Eθ {m(Z, θ)} = 0p×1 , Eθ {mT (Z, θ)m(Z, θ)} < ∞, and Eθ {m(Z, θ)mT (Z, θ)} is positive deﬁnite for all θ ∈ Ω. Additional regularity conditions are also necessary and will be deﬁned as we need them. The m-estimator θˆn is deﬁned as the solution (assuming it exists) of n

m(Zi , θˆn ) = 0

i=1

from a sample Z1 , . . . , Zn iid pZ (z, θ) θ ∈ Ω ⊂ Rp . Under suitable regularity conditions, the maximum likelihood estimator (MLE) of θ is an m-estimator. The MLE is deﬁned as the value θ that maximizes the likelihood n ! pZ (Zi , θ), i=1

or, equivalently, the value of θ that maximizes the log-likelihood n

log pZ (Zi , θ).

i=1

Under suitable regularity conditions, the maximum is found by taking the derivative of the log-likelihood with respect to θ and setting it equal to zero. That is, solving the score equation in θ, n i=1

Sθ (Zi , θ) = 0,

(3.5)

30

3 The Geometry of Influence Functions

where Sθ (z, θ) is the score vector (i.e., the derivative of the log-density) deﬁned in (3.3). Since the score vector Sθ (Z, θ), under suitable regularity conditions, has the property that Eθ {Sθ (Z, θ)} = 0 – see, for example, equation (7.3.8) of Casella and Berger (2002) – , this implies that the MLE is an example of an m-estimator. In order to prove the consistency and asymptotic normality of m-estimators, we need to assume certain regularity conditions. Some of the conditions that are discussed in Chapter 36 of the Handbook of Econometrics by Newey ∂m(Z, θ0 ) and McFadden (1994) include that E be nonsingular, where ∂θT ∂m(Zi , θ) is deﬁned as the p × p matrix of all partial derivatives of the ele∂θT ments of m(·) with respect to the elements of θ, and that n

−1

n ∂m(Zi , θ) i=1

∂θT

P

→ E θ0

∂m(Z, θ) ∂θT

uniformly in θ in a neighborhood of θ0 . For example, uniform convergence ∂m(Z, θ) are continuous in θ about would be satisﬁed if the sample paths of ∂θT θ0 almost surely and sup θ∈N (θ0 )

∂m(Z, θ) ≤ g(Z), ∂θT

E{g(Z)} < ∞,

where N (θ0 ) denotes a neighborhood in θ about θ0 . In fact, these regularity conditions would suﬃce to prove that the estimator θˆn is consistent; that is, P → θ0 . θˆn − Therefore, assuming that these regularity conditions hold, the inﬂuence function for θˆn is found by using the expansion "p×p n n n ∂m(Zi , θ∗ ) n ˆ 0= m(Zi , θn ) = m(Zi , θ0 ) + (θˆn − θ0 ), T ∂θ i=1 i=1 i=1 where θn∗ is an intermediate value between θˆn and θ0 . Because we have assumed suﬃcient regularity conditions to guarantee the consistency of θˆn , " n ∗ ∂m(Z , θ ) ∂m(Z, θ0 ) P i n −1 , n →E ∂θT ∂θT i=1 and by the nonsingularity assumption "−1 −1 n ∂m(Zi , θn∗ ) ∂m(Z, θ0 ) P −1 n → E . ∂θT ∂θT i=1

3.2 m-Estimators (Quick Review)

31

Therefore, n

1/2

$−1 # " n n ∂m(Zi , θn∗ ) −1 −1/2 ˆ n (θn − θ0 ) = − n m(Zi , θ0 ) ∂θT i=1 i=1 " −1 n ∂m(Zi , θ0 ) −1/2 n m(Zi , θ0 ) + op (1). =− E ∂θT i=1

Since, by deﬁnition, E{m(Z, θ0 )} = 0, we immediately deduce that the inﬂuence function of θˆn is given by −1 ∂m(Z, θ0 ) m(Zi , θ0 ) (3.6) − E ∂θT and n1/2 (θˆn − θ0 ) −→ (3.7) % −1 −1T & ∂m(Z, θ0 ) ∂m(Z, θ0 ) N 0, E , var m(Z, θ0 ) E ∂θT ∂θT D

where

' ( var {m(Z, θ0 )} = E m(Z, θ0 )mT (Z, θ0 ) .

Estimating the Asymptotic Variance of an m-Estimator In order to use an m-estimator for the parameter θ for practical applications, such as constructing conﬁdence intervals for the parameter θ or a subset of the parameter, we must be able to derive a consistent estimator for the asymptotic variance of θˆn . Under suitable regularity conditions, a consistent estimator for the asymptotic variance of θˆn can be derived intuitively using what is referred to as the “sandwich” variance estimator. This estimator is motivated by considering the asymptotic variance derived in (3.7). The following heuristic argument is used. If θ0 (the truth) is known, then a simple application of the weak law of ∂m(Z,θ0 ) large numbers can be used to obtain a consistent estimator for E , ∂θ T namely n ∂m(Zi , θ0 ) ˆ ∂m(Z, θ0 ) = n−1 , (3.8) E T ∂θ ∂θT i=1 and a consistent estimator for var{m(Z, θ0 )} can be obtained by using var{m(Z, ˆ θ0 )} = n−1

n i=1

m(Zi , θ0 )mT (Zi , θ0 ).

(3.9)

32

3 The Geometry of Influence Functions

Since θ0 is not known, we instead substitute θˆn for θ0 in equations (3.8) and (3.9) to obtain the sandwich estimator for the asymptotic variance, (3.7), of θˆn , given by # "$−1 "$−1T # ˆn ) ˆn ) ∂m(Z, θ ∂m(Z, θ ˆ ˆ E var ˆ m(Z, θˆn ) E . ∂θT ∂θT

(3.10)

The estimator (3.10) is referred to as the sandwich variance estimator as ˆ we see the term var(·) ˆ sandwiched between two terms involving E(·). The sandwich variance will be discussed in greater detail in Chapter 4 when we introduce estimators that solve generalized estimating equations (i.e., the socalled GEE estimators). For more details on m-estimators, we refer the reader to the excellent expository article by Stefanski and Boos (2002). When we consider the special case where the m-estimator is the MLE of θ (i.e., where m(Z, θ) = Sθ (Z, θ); see (3.5)), we note that − ∂m(Z,θ) = ∂θ T ∂Sθ (Z,θ) − ∂θT corresponds to minus the p × p matrix of second partial derivatives of the log-likelihood with respect to θ, which we denote by −Sθθ (Z, θ). Under suitable regularity conditions (see Section 7.3 of Casella and Berger, 2002), the information matrix, which we denote by I(θ0 ), is given by I(θ0 ) = Eθ0 {−Sθθ (Z, θ0 )} = Eθ0 {Sθ (Z, θ0 )SθT (Z, θ0 )}.

(3.11)

As a consequence of (3.6) and (3.7), we obtain the well-known results that the i-th inﬂuence function of the MLE is given by {I(θ0 )}−1 Sθ (Zi , θ0 ) and the asymptotic distribution is normal with mean zero and variance matrix equal to I −1 (θ0 ) (i.e., the inverse of the information matrix). Returning to the general m-estimator, since θ = (β T , η T )T and θˆn = (βˆnT , ηˆnT )T , the inﬂuence function of βˆn is made up of the ﬁrst q elements of the pdimensional inﬂuence function for θˆn given above. We will now illustrate why Corollary 1 applies to m-estimators. By deﬁnition, Eθ {m(Z, θ)} = 0p×1 . That is,

m(z, θ)p(z, θ)dν(z) = 0 for all θ.

Therefore, ∂ ∂θT

m(z, θ)p(z, θ)dν(z) = 0.

3.2 m-Estimators (Quick Review)

33

Assuming suitable regularity conditions that allow us to interchange integration and diﬀerentiation, we obtain ⎫ ⎧ ∂p(z, θ) ⎪ ⎪ ⎬ ⎨ ∂ ∂θT p(z, θ)dν(z) = 0. m(z, θ) p(z, θ)dν(z) + m(z, θ) ⎪ ∂θT θ) ⎪ ⎭ ⎩ p(z, * +, (3.12)

SθT (z, θ)

or the transpose of the score vector

At θ = θ0 , we deduce from equation (3.12) that ' ( ∂m(Z, θ0 ) = −E m(Z, θ0 )SθT (Z, θ0 ) , E T ∂θ which can also be written as −1 ' ( ∂m(Z, θ0 ) p×p I =− E E m(Z, θ0 )SθT (Z, θ0 ) , ∂θT

(3.13)

where I p×p denotes the p×p identity matrix. Recall that the inﬂuence function for θˆn , given by (3.6), is −1 ∂m(Z, θ0 ) ϕθˆn (Zi ) = − E m(Zi , θ0 ) ∂θT T and can be partitioned as ϕTβˆ (Zi ), ϕTηˆn (Zi ) . n

The covariance of the inﬂuence function ϕθˆn (Zi ) and the score vector Sθ (Zi , θ0 ) is E ϕθˆn (Zi )SθT (Zi , θ0 ) −1 ' ( ∂m(Z, θ0 ) =− E E m(Z, θ0 )SθT (Z, θ0 ) , (3.14) T ∂θ which by (3.13) is equal to I (q+r)×(q+r) , the identity matrix. This covariance matrix (3.14) can be partitioned as # $ ϕβˆn (Zi )SβT (Zi , θ0 ) ϕβˆn (Zi )SηT (Zi , θ0 ) E . ϕηˆn (Zi )SβT (Zi , θ0 ) ϕηˆn (Zi )SηT (Zi , θ0 ) Consequently, (i) E ϕβˆn (Zi )SβT (Zi , θ0 ) = I q×q

(the q × q identity matrix)

34

3 The Geometry of Influence Functions

and

(ii) E ϕβˆn (Zi )SηT (Zi , θ0 ) = 0q×r . Thus, we have veriﬁed that the two conditions of Corollary 1 hold for inﬂuence functions of m-estimators. Proof of Theorem 3.2 In order to prove Theorem 3.2, we must introduce the theory of contiguity, which we now review brieﬂy. An excellent overview of contiguity theory can be found in the Appendix of H´ ajek and Sidak (1967). Those readers not interested in the theoretical details can skip the remainder of this section. Deﬁnition 2. Let Vn be a sequence of random vectors and let P1n and P0n be sequences of probability measures with densities p1n (vn ) and p0n (vn ), respectively. The sequence of probability measures P1n is contiguous to the sequence of probability measures P0n if, for any sequence of events An deﬁned with respect to Vn , P0n (An ) → 0 as n → ∞ implies that P1n (An ) → 0 as n → ∞. In our applications, we let Vn = (Z1n , . . . , Znn ), where Z1n , . . . , Znn are iid random vectors and p0n (vn ) = p1n (vn ) =

n ! i=1 n !

p(zin , θ0 ), p(zin , θn ),

i=1

where n1/2 (θn − θ0 ) → τ , τ being a p-dimensional vector of constants. Letting the parameter θ0 denote the true value of the parameter that generates the data, then p1n (·) is an example of a local data generating process (LDGP) as given by Deﬁnition 1. If we could show that the sequence P1n is contiguous to the sequence P0n , then a sequence of random variables Tn (Vn ) that converges in probability to zero under the truth (i.e., for every > 0, P0n (|Tn | > ) → 0) would also satisfy that P1n (|Tn | > ) → 0; hence, Tn (Vn ) would converge in probability to zero for the LDGP. This fact can be very useful because in some problems it may be relatively easy to show that a sequence of random variables converges in probability to zero under the truth, in which case convergence in probability to zero under the LDGP follows immediately from contiguity. LeCam, in a series of lemmas (see H´ajek and Sidak, 1967), proved some important results regarding contiguity. One of LeCam’s results that is of particular use to us is as follows.

3.2 m-Estimators (Quick Review)

Lemma 3.1. LeCam If p1n (Vn ) D(P0n ) log −−−−−→ N (−σ 2 /2, σ 2 ), p0n (Vn )

35

(3.15)

then the sequence P1n is contiguous to the sequence P0n . Heuristic justiﬁcation of contiguity for LDGP To illustrate that (3.15) holds for LDGPs under suﬃcient smoothness and regularity conditions, we sketch out the following heuristic argument. Deﬁne p1n (Vn ) ! p(Zin , θn ) = . p0n (Vn ) i=1 p(Zin , θ0 ) n

Ln (Vn ) =

By a simple Taylor series expansion, we obtain log{Ln (Vn )} =

n

{log p(Zin , θn ) − log p(Zin , θ0 )}

i=1

= (θn − θ0 )T

n

" Sθ (Zin , θ0 )

i=1

n

+

(θn − θ0 )T {

i=1

Sθθ (Zin , θn∗ )}(θn − θ0 ) , 2

(3.16)

where Sθ (z, θ0 ) is the p-dimensional score vector deﬁned as ∂ log p(z, θ0 )/∂θ, Sθθ (z, θn∗ ) is the p × p matrix ∂ 2 log p(z, θn∗ )/∂θ∂θT , and θn∗ is some intermediate value between θn and θ0 . The expression (3.16) can be written as " n n1/2 (θn − θ0 )T n−1/2 Sθ (Zin , θ0 ) i=1

+

n1/2 (θn − θ0 )T {n−1

n

i=1

Sθθ (Zin , θn∗ )}n1/2 (θn − θ0 ) . 2

Under P0n : (i) Sθ (Zin , θ0 ), i = 1, . . . , n are iid mean zero random vectors with variance matrix equal to the information matrix I(θ0 ) deﬁned by (3.11). Consequently, by the CLT, n

−1/2

n

Sθ (Zin , θ0 ) −−−−−→ N 0, I(θ0 ) . D(P0n )

i=1

(ii) Since θn∗ → θ0 and Sθθ (Zin , θ0 ), i = 1, . . . , n are iid random matrices with mean −I(θ0 ), then, under suﬃcient smoothness conditions,

36

3 The Geometry of Influence Functions

n

−1

n

{Sθθ (Zin , θn∗ ) − Sθθ (Zin , θ0 )} − → 0, P

i=1

and by the weak law of large numbers n

−1

n

P

Sθθ (Zin , θ0 ) − → −I(θ0 ),

i=1

hence n

−1

n

Sθθ (Zin , θn∗ ) − → −I(θ0 ). P

i=1

By assumption, n imply that

1/2

(θn − θ0 ) → τ . Therefore, (i), (ii), and Slutsky’s theorem D(P0n )

log{Ln (Vn )} −−−−−→ N

τ T I(θ0 )τ T , τ I(θ0 )τ − 2

.

Consequently, by LeCam’s lemma, the sequence P1n is contiguous to the sequence P0n . Now we are in a position to prove Theorem 3.2. Proof. Theorem 3.2 . Consider the p0n (vn ) = p(zin , θ0 ) and the LDGP . sequence of densities p1n (vn ) = p(zin , θn ), where n1/2 (θn − θ0 ) → τ . By the deﬁnition of asymptotic linearity, n

1/2

{βˆn − β(θ0 )} = n−1/2

n

ϕ(Zin ) + oP0n (1),

(3.17)

i=1

where oP0n (1) is a sequence of random vectors that converge in probability to zero with respect to the sequence of probability measures P0n . Consider the LDGP deﬁned by θn . By contiguity, terms that are oP0n (1) are also oP1n (1). Consequently, by (3.17), n1/2 {βˆn − β(θ0 )} = n−1/2

n

ϕ(Zin ) + oP1n (1).

i=1

By adding and subtracting common terms, we obtain n1/2 {βˆn − β(θn )} = n−1/2

n

[ϕ(Zin ) − Eθn {ϕ(Z)}]

i=1

+n1/2 Eθn {ϕ(Z)} − n1/2 {β(θn ) − β(θ0 )} +oP1n (1).

(3.18)

3.2 m-Estimators (Quick Review)

By assumption, the estimator βˆn is regular; that is, D(P1n ) 1/2 ˆ T n {βn − β(θn )} −−−−−→ N 0, Eθ0 (ϕϕ ) .

37

(3.19)

Also, under P1n , [ϕ(Zin ) − Eθn {ϕ(Z)}], i = 1, . . . , n are iid mean-zero random vectors with variance matrix Eθn (ϕϕT ) − Eθn (ϕ)Eθn (ϕT ). By the smoothness assumption, Eθn (ϕϕT ) → Eθ0 (ϕϕT ) and Eθn (ϕ) → 0 as n → ∞. Hence, by the CLT, we obtain n−1/2

n

D(P1n ) [ϕ(Zin ) − Eθn {ϕ(Z)}] −−−−−→ N 0, Eθ0 (ϕϕT ) .

(3.20)

i=1

By a simple Taylor series expansion, we deduce that β(θn ) ≈ β(θ0 )+Γ(θ0 )(θn − θ0 ), where Γ(θ0 ) = ∂β(θ0 )/∂θT . Hence, n1/2 {β(θn ) − β(θ0 )} → Γ(θ0 )τ.

(3.21)

Finally, n1/2 Eθn {ϕ(Z)} = n1/2 =n

1/2

ϕ(z)p(z, θ0 )dν(z) + n

1/2

ϕ(z)p(z, θn )dν(z) ϕ(z)

n→∞

−−−−→ 0 +

ϕ(z)

∂p(z, θn∗ ) ∂θ

T (θn − θ0 )dν(z)

T ∂p(z, θ0 ) /p(z, θ0 ) p(z, θ0 )dν(z)τ ∂θ

= Eθ0 {ϕ(Z)SθT (Z, θ0 )}τ,

(3.22)

where θn∗ is some intermediate value between θn and θ0 . The only way that (3.19) and (3.20) can hold is if the limit of (3.18), as n → ∞, is identically equal to zero. By (3.21) and (3.22), this implies that Eθ0 {ϕ(Z)SθT (Z, θ0 )} − Γ(θ0 ) τ = 0q×1 . Since τ is arbitrary, this implies that Eθ0 {ϕ(Z)SθT (Z, θ0 )} = Γ(θ0 ), which proves Theorem 3.2.

We now show how the results of Theorem 3.2 lend themselves to a geometric interpretation that allows us to compare the eﬃciency of diﬀerent RAL estimators using our intuition of minimum distance and orthogonality.

38

3 The Geometry of Influence Functions

3.3 Geometry of Inﬂuence Functions for Parametric Models Consider the Hilbert space H of all q-dimensional measurable functions of Z with mean zero and ﬁnite variance equipped with the inner product

h1 , h2 = E(hT1 h2 ). We ﬁrst note that the score vector Sθ (Z, θ0 ), under suitable regularity conditions, has mean zero (i.e., E{Sθ (Z, θ0 )} = 0p×1 ). Similar to Example 2 of Chapter 2, we can deﬁne the ﬁnite-dimensional linear subspace T ⊂ H spanned by the p-dimensional score vector Sθ (Z, θ0 ) as the set of all q-dimensional mean-zero random vectors consisting of B q×p Sθ (Z, θ0 ) for all q × p matrices B. The linear subspace T is referred to as the tangent space. In the case where θ can be partitioned as (β T , η T )T , consider the linear subspace spanned by the nuisance score vector Sη (Z, θ0 ), B q×r Sη (Z, θ0 ),

(3.23)

for all q × r matrices B. This space is referred to as the nuisance tangent space and will be denoted by Λ. We note that condition (ii) of Corollary 1 is equivalent to saying that the q-dimensional inﬂuence function ϕβˆn (Z) for βˆn is orthogonal to the nuisance tangent space Λ. In addition to being orthogonal to the nuisance tangent space, the inﬂuence function of βˆn must also satisfy condition (i) of Corollary 1; namely, E ϕβˆn (Z)SβT (Z, θ0 ) = I q×q . Although inﬂuence functions of RAL estimators for β must satisfy conditions (i) and (ii) of Corollary 1, a natural question is whether the converse is true; that is, for any element of the Hilbert space satisfying conditions (i) and (ii) of Corollary 1, does there exist an RAL estimator for β with that inﬂuence function? Remark 2. To prove this in full generality, especially later when we consider inﬁnite-dimensional nuisance parameters, is diﬃcult and requires that some careful technical regularity conditions hold. Nonetheless, it may be instructive to see how one may, heuristically, construct estimators that have inﬂuence functions corresponding to elements in the subspace of the Hilbert space satisfying conditions (i) and (ii). Constructing Estimators Let ϕ(Z) be a q-dimensional measurable function with zero mean and ﬁnite variance that satisﬁes conditions (i) and (ii) of Corollary 1. Deﬁne

3.3 Geometry of Inﬂuence Functions for Parametric Models

39

m(Z, β, η) = ϕ(Z) − Eβ,η {ϕ(Z)}. Assume that we can ﬁnd a root-n consistent estimator for the nuisance paηn −η0 ) is bounded in probability). In many cases rameter ηˆn (i.e., where n1/2 (ˆ the estimator ηˆn will be β-dependent (i.e., ηˆn (β)). For example, we might use the MLE for η, or the restricted MLE for η, ﬁxing the value of β. We will now argue that the solution to the equation n

m{Zi , β, ηˆn (β)} = 0,

(3.24)

i=1

which we denote by βˆn , will be an asymptotically linear estimator with inﬂuence function ϕ(Z). By construction, we have Eβ0 ,η {m(Z, β0 , η)} = 0, or m(z, β0 , η)p(z, β0 , η)dν(z) = 0. Consequently, ∂ ∂η T

m(z, β0 , η)p(z, β0 , η)dν(z) = 0, η=η0

or

∂m(z, β0 , η0 ) p(z, β , η )dν(z) + m(z, β0 , η0 ) 0 0 ∂η T ×SηT (z, β0 , η0 )p(z, β0 , η0 )dν(z) = 0.

(3.25)

By deﬁnition, ϕ(Z) = m(Z, β0 , η0 ) must satisfy ( ' E ϕ(Z)SηT (Z, θ0 ) = 0. (This is condition (ii) of Corollary 1.) Consequently, by (3.25), we obtain ∂ m(Z, β0 , η0 ) = 0. (3.26) E ∂η T Similarly, we can show that ∂ E m(Z, β , η ) = −I q×q . 0 0 ∂β T A standard expansion yields

(3.27)

40

3 The Geometry of Influence Functions

0=

n

m{Zi , βˆn , ηˆn (βˆn )}

i=1

=

n

m{Zi , β0 , ηˆn (βˆn )}

i=1

$ n ∂m ∗ {Zi , βn , ηˆn (βˆn )} (βˆn − β0 ), + * +, ∂β T i=1 ⇓ Notice that this term is held ﬁxed #

(3.28)

where βn∗ is an intermediate value between βˆn and β0 . Therefore, n1/2 (βˆn − β0 ) # $−1 # $ n n ∂ −1 ∗ −1/2 ˆ ˆ n =− n m{Zi , βn , ηˆn (βn )} m{Zi , β0 , ηˆn (βn )} . ∂β T i=1 i=1 * +, ⇓p −1 ∂ m(Z, β , η ) = −I q×q by (3.27) E 0 0 ∂β T n

Let us consider the second term of (3.29); namely, n−1/2

(3.29)

m{Zi , β0 , ηˆn (βˆn )}.

i=1

By expansion, this equals n−1/2

n

m(Zi , β0 , η0 )

i=1

+

n

−1

n ∂m(Zi , β0 , η ∗ ) i=1

* E

"

n1/2 {ˆ ηn (βˆn ) − η0 } , * +, -

n

+,

∂η T

⇓p ∂ m(Z, β0 , η0 ) ∂η T = 0 by (3.26)

-

⇓ bounded in probability

where ηn∗ is an intermediate value between ηˆn (βˆn ) and η0 . Combining (3.29) and (3.30), we obtain n1/2 (βˆn − β0 ) = n−1/2 = n−1/2

n i=1 n i=1

m(Zi , β0 , η0 ) + op (1), ϕ(Zi ) + op (1),

(3.30)

3.3 Geometry of Inﬂuence Functions for Parametric Models

41

which illustrates that ϕ(Zi ) is the inﬂuence function for the i-th observation of the estimator βˆn above. Remark 3. This argument was independent of the choice of the root-n consistent estimator for the nuisance parameter η. Remark 4. In the derivation above, the asymptotic distribution of the estimator obtained by solving the estimating equation, which uses the estimating function m(Z, β, ηˆn ), is the same as the asymptotic distribution of the estimator solving the estimating equation using the estimating function m(Z, β, η0 ) had the true value of the nuisance parameter η0 been known to us. This fact follows from the orthogonality of the estimating function (evaluated at the truth) to the nuisance tangent space. This type of robustness, where the asymptotic distribution of an estimator is independent of whether the true value of the nuisance parameter is known or whether (and how) the nuisance parameter is estimated in an estimating equation, is one of the bonuses of working with estimating equations with estimating functions that are orthogonal to the nuisance tangent space. Remark 5. We want to make it clear that the estimator we just presented is for theoretical purposes only and not of practical use. The starting point was the choice of a function satisfying the conditions of Lemma 3.1. To ﬁnd such a function necessitates knowledge of the truth, which, of course, we don’t have. Nonetheless, starting with some truth, say θ0 , and some function ϕ(Z) satisfying the conditions of Corollary 1 (under the assumed true model), we constructed an estimator whose inﬂuence function is ϕ(Z) when θ0 is the truth. If, however, the data were generated, in truth, by some other value of the parameter, say θ∗ , then the estimator constructed by solving (3.24) would have some other inﬂuence function ϕ∗ (Z) satisfying the conditions of Lemma 3.1 at θ∗ . Thus, by Corollary 1, all RAL estimators have inﬂuence functions that belong to the subspace of our Hilbert space satisfying (i) E{ϕ(Z)SβT (Z, θ0 )} = I q×q and (ii) E{ϕ(Z)SηT (Z, θ0 )} = 0q×r , and, conversely, any element in the subspace above is the inﬂuence function of some RAL estimator. Why Is this Important? RAL estimators are asymptotically normally distributed; i.e., D 1/2 ˆ T n (βn − β0 ) −→ N 0, E(ϕϕ ) .

42

3 The Geometry of Influence Functions

Because of this, we can compare competing RAL estimators for β by looking at the asymptotic variance, where clearly the better estimator is the one with smaller asymptotic variance. We argued earlier, however, that the asymptotic variance of an RAL estimator is the variance of its inﬂuence function. Therefore, it suﬃces to consider the variance of inﬂuence functions. We already illustrated that inﬂuence functions can be viewed as elements in a subspace of a Hilbert space. Moreover, in this Hilbert space the distance to the origin (squared) of any element (random function) is the variance of the element. Consequently, the search for the best estimator (i.e., the one with the smallest asymptotic variance) is equivalent to the search for the element in the subspace of inﬂuence functions that has the shortest distance to the origin. Remark 6. We want to emphasize again that Hilbert spaces are characterized by both the elements that make up the space (random functions in our case) and the inner product, h1 , h2 = E(hT1 h2 ), where expectation is always taken with respect to the truth (θ0 ). Therefore, for diﬀerent θ0 , we have diﬀerent Hilbert spaces. This also means that the subspace that deﬁnes the class of inﬂuence functions is θ0 -dependent.

3.4 Eﬃcient Inﬂuence Function We will show how the geometry of Hilbert spaces will allow us to identify the most eﬃcient inﬂuence function (i.e., the inﬂuence function with the smallest variance). First, however, we give some additional notation and deﬁnitions regarding operations on linear subspaces that will be needed shortly. Deﬁnition 3. We say that M ⊕ N is a direct sum of two linear subspaces M ⊂ H and N ⊂ H if M ⊕ N is a linear subspace in H and if every element x ∈ M ⊕ N has a unique representation of the form x = m + n, where m ∈ M and n ∈ N . Deﬁnition 4. The set of elements of a Hilbert space that are orthogonal to a linear subspace M is denoted by M ⊥ . The space M ⊥ is also a linear subspace (referred to as the orthogonal complement of M ) and the entire Hilbert space H = M ⊕ M ⊥. Condition (ii) of Corollary 1 can now be stated as follows: If ϕ(Z) is an inﬂuence function of an RAL estimator, then ϕ ∈ Λ⊥ , where Λ denotes the nuisance tangent space deﬁned by (3.23). Deﬁnition 5. If we consider any arbitrary element h(Z) ∈ H, then by the projection theorem, there exists a unique element a0 (Z) ∈ Λ such that h−a0 has the minimum norm and a0 must uniquely satisfy the relationship

h − a0 , a = 0 for all a ∈ Λ.

3.4 Eﬃcient Inﬂuence Function

43

The element a0 is referred to as the projection of h onto the space Λ and is denoted by Π(h|Λ). The element with the minimum norm, h−a0 , is sometimes referred to as the residual of h after projecting onto Λ, and it is easy to show that h − a0 = Π(h|Λ⊥ ). As we discussed earlier, condition (ii) of Corollary 1 is equivalent to an element h(Z) in our Hilbert space H being orthogonal to the nuisance tangent space; i.e., the linear subspace generated by the nuisance score vector, namely Λ = {B q×r Sη (Z, θ0 ) for all B q×r }. If we want to identify all elements orthogonal to the nuisance tangent space, we can consider the set of elements h − Π(h|Λ) for all h ∈ H, where using the results in Example 2 of Chapter 2, Π(h|Λ) = E(hSηT ){E(Sη SηT )}−1 Sη (Z, θ0 ). It is also straightforward to show that the tangent space T = {B q×p Sθ (Z, θ0 ) for all B q×p } can be written as the direct sum of the nuisance tangent space and the tangent space generated by the score vector with respect to the parameter of interest “β”. That is, if we deﬁne Tβ as the space {B q×q Sβ (Z, θ0 ) for all B q×q }, then T = Tβ ⊕ Λ. Asymptotic Variance when Dimension Is Greater than One When the parameter of interest β has dimension ≥ 2, we must be careful about what we mean by smaller asymptotic variance for an estimator or its inﬂuence function. Consider two RAL estimators for β with inﬂuence function ϕ(1) (Z) and ϕ(2) (Z), respectively. We say that var {ϕ(1) (Z)} ≤ var {ϕ(2) (Z)} if and only if

var {aT ϕ(1) (Z)} ≤ var {aT ϕ(2) (Z)}

for all q × 1 constant vectors a. Equivalently, T

T

aT E{ϕ(1) (Z)ϕ(1) (Z)}a ≤ aT E{ϕ(2) (Z)ϕ(2) (Z)}a. This means that T

T

aT [E{ϕ(2) (Z)ϕ(2) (Z)} − E{ϕ(1) (Z)ϕ(1) (Z)}] a ≥ 0, T

T

or E(ϕ(2) ϕ(2) ) − E(ϕ(1) ϕ(1) ) is nonnegative deﬁnite.

44

3 The Geometry of Influence Functions

If H(1) is the Hilbert space of one-dimensional mean-zero random functions of Z, where we use the superscript (1) to emphasize one-dimensional random functions, and if h1 and h2 are elements of H(1) that are orthogonal to each other, then, by the Pythagorean theorem, we know that var(h1 + h2 ) = var(h1 ) + var(h2 ), making it clear that var(h1 + h2 ) is greater than or equal to var(h1 ) or var(h2 ). Unfortunately, when H consists of qdimensional mean-zero random functions, there is no such general relationship with regard to the variance matrices. However, there is an important special case when this does occur, which we now discuss. Deﬁnition 6. q-replicating linear space A linear subspace U ⊂ H is a q-replicating linear space if U is of the form U (1) × . . . × U (1) or {U (1) }q , where U (1) denotes a linear subspace in H(1) and {U (1) }q ⊂ H represents the linear subspace in H that consists of elements h = (h(1) , . . . , h(q) )T such that h(j) ∈ U (1) for all j = 1, . . . , q; i.e., {U (1) }q consists of q-dimensional random functions, where each element in the vector is an element of U (1) , or the space U (1) stacked up on itself q times. The linear subspace spanned by an r-dimensional vector of mean zero ﬁnite variance random functions v r×1 (Z), namely the subspace S = {B q×r v(Z) : for all constant matrices B q×r }, is such a subspace. This is easily seen by deﬁning U (1) to be the space {bT v(Z) : for all constant r-dimensional constant vectors br×1 }, in which case S = {U (1) }q . Since tangent spaces and nuisance tangent spaces are linear subspaces spanned by score vectors, these are examples of qreplicating linear spaces. Theorem 3.3. Multivariate Pythagorean theorem If h ∈ H and is an element of a q-replicating linear space U, and ∈ H is orthogonal to U, then var( + h) = var() + var(h),

(3.31)

T

where var(h) = E(hh ). As a consequence of (3.31), we obtain a multivariate version of the Pythagorean theorem; namely, for any h∗ ∈ H, var(h∗ ) = var (Π[h∗ |U]) + var (h∗ − Π[h∗ |U]) .

(3.32)

Proof. It is easy to show that an element = ((1) , . . . , (q) )T ∈ H is orthogonal to U = {U (1) }q if and only if each element (j) , j = 1, . . . , q is orthogonal to U (1) . Consequently, such an element is not only orthogonal to h ∈ {U (1) }q in the sense that E(T h) = 0 but also in that E(hT ) = E(hT ) = 0q×q . This is important because for such an and h, we obtain var( + h) = var() + var(h), where var(h) = E(hhT ).

3.4 Eﬃcient Inﬂuence Function

45

This means that, for such cases, the variance matrix of +h, for q-dimensional and h, is larger (in the multidimensional sense deﬁned above) than either the variance matrix of or the variance matrix of h. In many of the arguments that follow, we will be decomposing elements of the Hilbert space as the projection to a tangent space or a nuisance tangent space plus the residual after the projection. For such problems, because the tangent space or nuisance tangent space is a q-replicating linear space, we now know that we can immediately apply the multivariate version of the Pythagorean theorem where the variance matrix of any element is always larger than the variance matrix of the projection or the variance matrix of the residual after projection. Consequently, we don’t have to distinguish between the Hilbert space of one-dimensional random functions and q-dimensional random functions. Geometry of Inﬂuence Functions Before describing the geometry of inﬂuence functions, we ﬁrst give the deﬁnition of a linear variety (sometimes also called an aﬃne space). Deﬁnition 7. A linear variety is the translation of a linear subspace away from the origin; i.e., a linear variety V can be written as V = x0 + M , where / M, x0 = 0, and M is a linear subspace (see Figure 3.4). x0 ∈ H and x0 ∈

V

M (linear subspace)

Fig. 3.4. Depiction of a linear variety

Theorem 3.4. The set of all inﬂuence functions, namely the elements of H that satisfy condition (3.4) of Theorem 3.2, is the linear variety ϕ∗ (Z) + T ⊥ , where ϕ∗ (Z) is any inﬂuence function and T ⊥ is the space perpendicular to the tangent space. Proof. Any element l(Z) ∈ T ⊥ must satisfy E{l(Z)SθT (Z, θ0 )} = 0q×p . Therefore, if we take

(3.33)

46

3 The Geometry of Influence Functions

ϕ(Z) = ϕ∗ (Z) + l(Z), then E{ϕ(Z)SθT (Z, θ0 )} = E {ϕ∗ (Z) + l(Z)} SθT (Z, θ0 ) = E ϕ∗ (Z)SθT (Z, θ0 ) + E l(Z)SθT (Z, θ0 ) = Γ(θ0 ) + 0q×p = Γ(θ0 ). Hence, ϕ(Z) is an inﬂuence function satisfying condition (3.4) of Theorem 3.2. Conversely, if ϕ(Z) is an inﬂuence function satisfying (3.4) of Theorem 3.2, then ϕ(Z) = ϕ∗ (Z) + {ϕ(Z) − ϕ∗ (Z)}. It is a simple exercise to verify that {ϕ(Z) − ϕ∗ (Z)} ∈ T ⊥ . Deriving the Eﬃcient Inﬂuence Function The eﬃcient inﬂuence function ϕeﬀ (Z), if it exists, is the inﬂuence function with the smallest variance matrix; that is, for any inﬂuence function ϕ(Z) = ϕeﬀ (Z), var{ϕeﬀ (Z)} − var{ϕ(Z)} is negative deﬁnite. That an efﬁcient inﬂuence function exists and is unique is now easy to see from the geometry of the problem. Theorem 3.5. The eﬃcient inﬂuence function is given by ϕeﬀ (Z) = ϕ∗ (Z) − Π(ϕ∗ (Z)|T ⊥ ) = Π(ϕ∗ (Z)|T ),

(3.34)

where ϕ∗ (Z) is an arbitrary inﬂuence function and T is the tangent space, and can explicitly be written as ϕeﬀ (Z) = Γ(θ0 )I −1 (θ0 )Sθ (Z, θ0 ).

(3.35)

Proof. By Theorem 3.4, the class of inﬂuence functions is a linear variety, ϕ∗ (Z) + T ⊥ . Let ϕeﬀ = ϕ∗ − Π(ϕ∗ |T ⊥ ) = Π(ϕ∗ |T ). Because Π(ϕ∗ |T ⊥ ) ∈ T ⊥ , this implies that ϕeﬀ is an inﬂuence function and, moreover, is orthogonal to T ⊥ . Consequently, any other inﬂuence function can be written as ϕ = ϕeﬀ + l, with l ∈ T ⊥ . The tangent space T and its orthogonal complement T ⊥ are examples of q-replicating linear spaces as deﬁned by Deﬁnition 6. Therefore, because of Theorem 3.3, equation (3.31), we obtain var(ϕ) = var(ϕeﬀ )+var(l), which demonstrates that ϕeﬀ , constructed as above, is the eﬃcient inﬂuence function. We deduce from the argument above that the eﬃcient inﬂuence function ϕeﬀ = Π(ϕ∗ |T ) is an element of the tangent space T and hence can be exq×p q×p pressed as ϕeﬀ (Z) = Beﬀ Sθ (Z, θ0 ) for some constant matrix Beﬀ . Since ϕeﬀ (Z) is an inﬂuence function, it must also satisfy relationship (3.4) of Theorem 3.2; i.e., E{ϕeﬀ (Z)SθT (Z, θ0 )} = Γ(θ0 ),

3.4 Eﬃcient Inﬂuence Function

47

or Beﬀ E{Sθ (Z, θ0 )SθT (Z, θ0 )} = Γ(θ0 ), which implies

Beﬀ = Γ(θ0 )I −1 (θ0 ),

where I(θ0 ) = E{Sθ (Z, θ0 )SθT (Z, θ0 )} is the information matrix. Consequently, the eﬃcient inﬂuence function is given by ϕeﬀ (Z) = Γ(θ0 )I −1 (θ0 )Sθ (Z, θ0 ). It is instructive to consider the special case θ = (β T , η T )T . We ﬁrst deﬁne the important notion of an eﬃcient score vector and then show the relationship of the eﬃcient score to the eﬃcient inﬂuence function. Deﬁnition 8. The eﬃcient score is the residual of the score vector with respect to the parameter of interest after projecting it onto the nuisance tangent space; i.e., Seﬀ (Z, θ0 ) = Sβ (Z, θ0 ) − Π(Sβ (Z, θ0 )|Λ). Recall that Π(Sβ (Z, θ0 )|Λ) = E(Sβ SηT ){E(Sη SηT )}−1 Sη (Z, θ0 ). Corollary 2. When the parameter θ can be partitioned as (β T , η T )T , where β is the parameter of interest and η is the nuisance parameter, then the eﬃcient inﬂuence function can be written as T )}−1 {Seﬀ (Z, θ0 )}. ϕeﬀ (Z, θ0 ) = {E(Seﬀ Seﬀ

(3.36)

Proof. By construction, the eﬃcient score vector is orthogonal to the nuisance tangent space; i.e., it satisﬁes condition (ii) of being an inﬂuence function. By appropriately scaling the eﬃcient score, we can construct an inﬂuence function, which we will show is the eﬃcient inﬂuence function. We ﬁrst note T (Z, θ0 )}. This follows because that E{Seﬀ (Z, θ0 )SβT (Z, θ0 )} = E{Seﬀ (Z, θ0 )Seﬀ T E{Seﬀ (Z, θ0 )SβT (Z, θ0 )} = E{Seﬀ (Z, θ0 )Seﬀ (Z, θ0 )} + E{Seﬀ (Z, θ0 )Π(Sβ |Λ)T } . * +, -

This equals zero since Seﬀ (Z, θ0 } ⊥ Λ Therefore, if we deﬁne T )}−1 Seﬀ (Z, θ0 ), ϕeﬀ (Z, θ0 ) = {E(Seﬀ Seﬀ

then (i) E[ϕeﬀ (Z, θ0 )SβT (Z, θ0 )] = I q×q and

48

3 The Geometry of Influence Functions

(ii) E[ϕeﬀ (Z, θ0 )SηT (Z, θ0 )] = 0q×r ; i.e., ϕeﬀ (Z, θ0 ) satisﬁes conditions (i) and (ii) of Corollary 1 and thus is an inﬂuence function. As argued above, the eﬃcient inﬂuence function is the unique inﬂuence function belonging to the tangent space T . Since both Sβ (Z, θ0 ) and Π(Sβ (Z, θ0 )|Λ) are elements of T , so is T )}−1 {Sβ (Z, θ0 ) − Π(Sβ |Λ)}, ϕeﬀ (Z, θ0 ) = {E(Seﬀ Seﬀ

thus demonstrating that (3.36) is the eﬃcient inﬂuence function for RAL estimators of β. Remark 7. When the parameter θ can be partitioned as (β T , η T )T , then Γ(θ0 ) can be partitioned as [I q×q : 0q×r ], and it is a straightforward exercise to show that (3.35) leads to (3.36). Remark 8. If we denote by (βˆnM LE , ηˆnM LE ) the values of β and η that maximize the likelihood n ! p(Zi , β, η), i=1

then under suitable regularity conditions, the estimator βˆnM LE of β is an RAL estimator whose inﬂuence function is the eﬃcient inﬂuence function given by (3.36). See Exercise 3.2 below. Remark 9. If the parameter of interest is given by β(θ) and we deﬁne by θˆnM LE the value of θ that maximizes the likelihood n !

p(Zi , θ),

i=1

then, under suitable regularity conditions, the estimator β(θˆnM LE ) of β is an RAL estimator with eﬃcient inﬂuence function (3.35). Remark 10. By deﬁnition, T )}−1 Seﬀ (Z, θ0 ) ϕeﬀ (Z, θ0 ) = {E(Seﬀ Seﬀ

has variance equal to

T )}−1 , {E(Seﬀ Seﬀ

the inverse of the variance matrix of the eﬃcient score. If we deﬁne Iββ = E(Sβ SβT ), Iηη = E(Sη SηT ), and Iβη = E(Sβ SηT ), then we obtain the wellknown result that the minimum variance for the most eﬃcient RAL estimator is −1 T −1 {Iββ − Iβη Iηη Iβη } , where Iββ , Iβη , Iηη are elements of the information matrix used in likelihood theory.

3.5 Review of Notation for Parametric Models

49

3.5 Review of Notation for Parametric Models We now give a quick review of some of the notation and ideas developed in Chapter 3 as a useful reference. –

Z1 , . . . , Zn iid p(z, β, η), β ∈ Rq , η ∈ Rr , θ = (β T, η T )T , θ ∈ Rp , p = q + r.

– Truth is denoted as θ0 = (β0T , η0T )T . – n1/2 (βˆn −β0 ) = n−1/2 Σϕ(Zi )+op (1), where ϕ(Zi ) is the inﬂuence function for the i-th observation of βˆn . – Hilbert space: q-dimensional measurable functions of Z with mean zero and ﬁnite variance equipped with the covariance inner product E{hT1 (Z)h2 (Z)}. – Score vector: For θ = (β T , η T )T , Sβ (z) = Sη (z) = Sθ (z) =

∂ log p(z, θ) ∂β ∂ log p(z, θ) ∂η ∂ log p(z, θ) ∂θ

, θ0

, θ0

= {SβT (z), SηT (z)}T . θ0

Linear subspaces Nuisance tangent space: Λ = {B q×r Sη : for all B q×r }. Tangent space: T = {B q×p Sθ : for all B q×p }, T = Tβ ⊕ Λ, where Tβ = {B q×q Sβ : for all B q×q }, and ⊕ denotes the direct sum of linear subspaces. Inﬂuence functions ϕ must satisfy (i) E{ϕSβT } = I q×q and (ii) E{ϕSηT } = 0q×r ; ϕ ⊥ Λ, ϕ ∈ Λ⊥ .

50

•

3 The Geometry of Influence Functions

Eﬃcient score Seﬀ (Z, θ0 ) = Sβ (Z, θ0 ) − Π(Sβ |Λ); Π(Sβ |Λ) = E(Sβ SηT ){E(Sη SηT )}−1 Sη (Z, θ0 ).

•

Eﬃcient inﬂuence function T ϕeﬀ (Z) = {E(Seﬀ Seﬀ )}−1 Seﬀ (Z, θ0 ).

• Any inﬂuence function is equal to ϕ(Z) = ϕeﬀ (Z) + l(Z), l(Z) ∈ T ⊥ . That is, inﬂuence functions lie on a linear variety and •

E(ϕϕT ) = E(ϕeﬀ ϕTeﬀ ) + E(llT ).

3.6 Exercises for Chapter 3 1. Prove that the Hodges super-eﬃcient estimator µ ˆn , given in Section 3.1, is not asymptotically regular. 2. Let Z1 , . . . , Zn be iid p(z, β, η), where β ∈ Rq and η ∈ Rr . Assume all the usual regularity conditions that allow the maximum likelihood estimator to be a solution to the score equation, n Sβ (Zi , β, η) = 0(q+r)×1 , Sη (Zi , β, η) i=1

and be consistent and asymptotically normal. a) Show that the inﬂuence function for βˆn is the eﬃcient inﬂuence function. b) Sketch out an argument that shows that the solution to the estimating equation n q×1 Seﬀ {Zi , β, ηˆn∗ (β)} = 0q×1 , i=1

for any root-n consistent estimator ηˆn∗ (β), yields an estimator that is asymptotically linear with the eﬃcient inﬂuence function. 3. Assume Y1 , . . . , Yn are iid with distribution function F (y) = P (Y ≤ y), which is diﬀerentiable everywhere with density f (y) = dFdy(y) . The median is deﬁned as β = F −1 12 . The sample median is deﬁned as 1 , βˆn ≈ Fˆn−1 2

3.6 Exercises for Chapter 3

51

n where Fˆn (y) = n−1 i=1 I(Yi ≤ y) is the empirical distribution function. Equivalently, βˆn is the solution to the m-estimating equation n i=1

1 I(Yi ≤ β) − 2

≈ 0.

Remark 11. We use “≈” to denote approximately because the estimating equation is not continuous in β and therefore will not always yield a solution. However, for large n, you can get very close to zero, the diﬀerence being asymptotically negligible. (a) Find the inﬂuence function for the sample median βˆn . Hint: You may assume the following to get your answer. (i) βˆn is consistent; i.e., βˆn → β0 = F −1 12 . (ii) Stochastic equicontinuity: P n1/2 Fˆn (βˆn ) − F (βˆn ) − n1/2 Fˆn (β0 ) − F (β0 ) − → 0. (b) Let Y1 , . . . , Yn be iid N (µ, σ 2 ), µ ∈ R, σ 2 > 0. Clearly, for this model, the median β is equal to µ. Verify, by direct calculation, that the inﬂuence function for the sample median satisﬁes the two conditions of Corollary 1.

4 Semiparametric Models

In Chapter 3, we developed theoretical results for estimators of parameters in ﬁnite-dimensional parametric models where Z1 , . . . , Zn are iid {p(z, θ), θ ∈ Ω ⊂ Rp }, p ﬁnite, and where θ can be partitioned as θ = (β T , η T )T

β ∈ R q , η ∈ Rr

p = q + r,

β being the parameter of interest and η the nuisance parameter. In this chapter, we will extend this theory to semiparametric models, where the parameter space for θ is inﬁnite-dimensional. For most of the exposition in this book, as well as most of the examples used throughout, we will consider semiparametric models that can be represented using the class of densities p(z, β, η), where β, the parameter of interest, is ﬁnite-dimensional (q-dimensional); η, the nuisance parameter, is inﬁnite-dimensional; and β and η are variationally independent – that is, any choice of β and η in a neighborhood about the true β0 and η0 would result in a density p(z, β, η) in the semiparametric model. This will allow us, for example, to explicitly deﬁne partial derivatives ∂p(z, β, η0 ) ∂β

= β=β0

∂p(z, β0 , η0 ) . ∂β

Keep in mind, however, that some problems lend themselves more naturally to models represented by the class of densities p(z, θ), where θ is inﬁnitedimensional and the parameter of interest, β q×1 (θ), is a smooth q-dimensional function of θ. When the second representation is easier to work with, we will make the distinction explicit. In Chapter 1, we gave two examples of semiparametric models: (i) Restricted moment model Yi = µ(Xi , β) + εi , E(εi |Xi ) = 0,

54

4 Semiparametric Models

or equivalently E(Yi |Xi ) = µ(Xi , β). (ii) Proportional hazards model The hazard of failing at time t is λ(t|Xi ) = λ(t) exp(β T Xi ). The major aim is to ﬁnd “good” semiparametric estimators for β, where “loosely speaking” a semiparametric estimator has the property that D{p(·,β,η)} n1/2 (βˆn − β) −−−−−−−→ N 0, Σq×q (β, η) for all densities “p(·, β, η)” within some semiparametric model and “good” refers to estimators with small asymptotic variance. All of these ideas will be made precise shortly.

4.1 GEE Estimators for the Restricted Moment Model The restricted moment model was introduced brieﬂy in Section 1.2. In this model, we are primarily interested in studying the relationship of a response variable Y , possibly vector-valued, as a function of covariates X. Speciﬁcally, the restricted moment model considers the conditional expectation of Y given X; that is, E(Y d×1 |X) = µd×1 (X, β) through the function µ(X, β) of X and the q-dimensional parameter β. Here, “d” denotes the dimension of the response variable Y . Therefore, the restricted moment model allows the modeling of multivariate and longitudinal response data as a function of covariates (i.e., d > 1) as well as more traditional regression models for a univariate response variable (i.e., d = 1). An example of a semiparametric estimator for the restricted moment model is the solution to the linear estimating equation n

Aq×d (Xi , βˆn ) Yid×1 − µd×1 (Xi , βˆn ) = 0q×1 ,

(4.1)

i=1

where A(Xi , β) is an arbitrary (q × d) matrix of functions of the covariate Xi and the parameter β. Subject to suitable regularity conditions, βˆn is a consistent, asymptotically normal estimator for β and thus is an example of a semiparametric estimator for the restricted moment model. Such an estimator is an example of a solution to a generalized estimating equation, or GEE, as deﬁned by Liang and Zeger (1986). It is also an example of an m-estimator as deﬁned in Chapter 3. For completeness, we sketch out a heuristic argument for the asymptotic normality of βˆn and describe how to estimate its asymptotic variance.

4.1 GEE Estimators for the Restricted Moment Model

55

Asymptotic Properties for GEE Estimators The asymptotic properties of the GEE estimator follow from the expansion n

0=

A(Xi , βˆn ){Yi − µ(Xi , βˆn )}

i=1 n

=

A(Xi , β0 ){Yi − µ(Xi , β0 )}

i=1

+

n

Q(Yi , Xi , βn∗ ) −

i=1

n

" A(Xi , βn∗ ) D(Xi , βn∗ ) (βˆn − β0 ),

(4.2)

i=1

where

∂µ(X, β) ∂β T

D(X, β) =

(4.3)

is the gradient matrix (d × q), made up of all partial derivatives of the delements of µ(X, β) with respect to the q-elements of β, and βn∗ denotes some intermediate value between βˆn and β0 . If we denote the rows of A(Xi , β) by {A1 (Xi , β), . . . , Aq (Xi , β)}, then Qq×q (Yi , Xi , β) is the q × q matrix deﬁned by ⎞ ∂AT (X ,β) {Yi − µ(Xi , β)}T 1∂β Ti ⎟ ⎜ .. ⎟. Qq×q (Yi , Xi , β) = ⎜ . ⎠ ⎝ T ∂A (Xi ,β) {Yi − µ(Xi , β)}T q∂β T ⎛

This matrix, although complicated, is made up of a linear combination of functions of Xi multiplied by elements of {Yi − µ(Xi , β)}, which, as we will demonstrate shortly, drops out of consideration for the asymptotic theory. Using (4.2), we obtain n n1/2 (βˆn − β0 ) = −n−1 Q(Yi , Xi , β ∗ ) n

i=1

+n−1

n

"−1

A(Xi , βn∗ )D(Xi , βn∗ )

n−1/2

i=1

n

A(Xi , β0 ){Yi − µ(X, β0 )}.

i=1

Because n−1

n

Q(Yi , Xi , βn∗ ) → E {Q(Y, X, β0 )} = 0 P

i=1

and n−1

n i=1

A(Xi , βn∗ )D(Xi , βn∗ ) − → E{A(X, β0 )D(X, β0 )}, P

56

4 Semiparametric Models

we obtain n

1/2

(βˆn − β0 ) = n−1/2

n i=1

−1

[E{A(X, β0 )D(X, β0 )}]

A(Xi , β0 )

× {Yi − µ(Xi , β0 )} + op (1).

Consequently, the inﬂuence function for the i-th observation of βˆn is {E(AD)}−1 A(Xi , β0 ){Yi − µ(Xi , β0 )}.

(4.4)

As we demonstrated in Chapter 3, the asymptotic variance of an RAL estimator for β is the variance of its inﬂuence function. Therefore, the asymptotic variance of the GEE estimator βˆn is the variance of (4.4). We ﬁrst compute the variance of A(Xi , β0 ){Yi − µ(Xi , β0 )}, which equals var A(Xi , β0 ){Yi − µ(Xi , β0 )} = E var [A(Xi , β0 ){Yi − µ(Xi , β0 )}|Xi ] + var E [A(Xi , β0 ){Yi − µ(Xi , β0 )}|Xi ] * +,

0 T

= E{A(Xi , β0 )V (Xi )A (Xi , β0 )},

(4.5)

where V (Xi ) = var(Yi |Xi ) is the d × d conditional variance matrix of Yi given Xi . Consequently, the asymptotic variance of βˆn is −1T

{E(AD)}−1 E{AV (X)AT } {E(AD)}

.

(4.6)

In order to use the results above for data analytic applications, such as constructing conﬁdence intervals for β or for some components of β, we must also be able to derive consistent estimators for the asymptotic variance of βˆn given by (4.6). Without going into all the technical details, we now outline the arguments for constructing such an estimator. These arguments are similar to those that resulted in the sandwich variance estimator for the asymptotic variance of an m-estimator given by (3.10) in Chapter 3. Suppose, for the time being, that we assumed the true-valued β0 were known to us. Then, by the law of large numbers, a consistent estimator for E(AD) is given by ˆ0 (AD) = n−1 E

n

A(Xi , β0 )D(Xi , β0 ),

(4.7)

i=1

where the subscript “0” is used to emphasize that this statistic is computed with β0 known. As we showed in (4.5), the variance of A(Xi , β0 ){Yi −

4.1 GEE Estimators for the Restricted Moment Model

57

µ(Xi , β0 )} is given by E{A(Xi , β0 )V (Xi )AT (Xi , β0 )}, which, by the law of large numbers, can be consistently estimated by ˆ0 (AV AT ) = n−1 E

n

A(Xi , β0 ){Yi − µ(Xi , β0 )}{Yi − µ(Xi , β0 )}T AT (Xi , β0 ).

i=1

(4.8) Of course, the value β0 is not known to us. But since βˆn is a consistent estimator for β0 , a natural estimator for the asymptotic variance of βˆn is given by −1 ˆ −1T ˆ ˆ , (4.9) {E(AD)} E(AV AT ){E(AD)} ˆ ˆ where E(AD) and E(AV AT ) are computed as in equations (4.7) and (4.8), ˆ respectively, with βn substituted for β0 . The estimator (4.9) is referred to as the sandwich estimator for the asymptotic variance. More details about this methodology can be found in Liang and Zeger (1986). The results above did not depend on any speciﬁc parametric assumptions beyond the moment restriction and regularity conditions. Consequently, the estimator, given as the solution to equation (4.1), is a semiparametric estimator for the restricted moment model. Example: Log-linear Model Consider the problem where we want to model the relationship of a response variable Y , which is positive, as a function of covariates X. For example, in the study of HIV disease, CD4 count is a measure of the degree of destruction that HIV disease has on the immune system. Therefore, it may be of interest to model CD4 count as a function of covariates such as treatment, age, race, etc. Let us denote by X = (X1 , . . . , Xq−1 )T the q − 1 vector of covariates that we are considering. Because CD4 count is a positive random variable, a popular model is the log-linear model where it is assumed that log{E(Y |X)} = α + δ1 X1 + . . . + δq−1 Xq−1 . Here, the parameter of interest is given by β q×1 = (α, δ1 , . . . , δq−1 )T . This is an example of a restricted moment model where E(Y |X) = µ(X, β) = exp(α + δ1 X1 + . . . + δq−1 Xq−1 ).

(4.10)

The log transformation guarantees that the conditional mean response, given the covariates, is always positive. Consequently, this model puts no restrictions on the possible values that β can take. With a sample of iid data (Yi , Xi ), i = 1, . . . , n, a semiparametric estimator for β can be obtained as the solution to a generalized estimating equation given by (4.1). In this example, the response variable Y is a single random variable; hence d = 1. If we take Aq×1 (X, β) in (4.1) to equal (1, X1 , . . . , Xq−1 )T ,

58

4 Semiparametric Models

then the corresponding GEE estimator βˆn = (ˆ αn , . . . , δˆ(q−1)n )T is the solution to n

(1, XiT )T {Yi − exp(α + δ1 X1i + . . . + δq−1 X(q−1)i )} = 0q×1 .

(4.11)

i=1

The estimator βˆn is consistent and asymptotically normal with a variance matrix that can be estimated using (4.9), where the derivative matrix ˆ D1×q (X, β), deﬁned by (4.3), is equal to µ(X, β)(1, X T ), and E(AD) and T ˆ E(AV A ) are given by equations (4.7) and (4.8), respectively, with βˆn substituted for β0 . Speciﬁcally, ˆ E(AD) = n−1

n

(1, XiT )T µ(Xi , βˆn )(1, XiT ),

(4.12)

i=1

ˆ E(AV AT ) = n−1

n

(1, XiT )T {Yi − µ(Xi , βˆn )}2 (1, XiT ).

(4.13)

i=1

Remark 1. The asymptotic variance is the variance matrix of the limiting normal distribution to which n1/2 (βˆn − β0 ) converges. That is, the asymptotic D variance is equal to the (q × q) matrix Σ, where n1/2 (βˆn − β0 ) −→ N (0, Σ). ˆ n and is given by The estimator for the asymptotic variance is denoted by Σ −1 ˆ −1 ˆ n = {E(AD)} ˆ ˆ Σ , E(AV AT ){E(AD)}

(4.14)

ˆ ˆ where E(AD) and E(AV AT ) are deﬁned by (4.12) and (4.13), respectively. For practical applications, say, when we are constructing conﬁdence intervals for δj , the regression coeﬃcient for the j-th covariate Xj , j = 1, . . . , q −1, we must be careful to use the appropriate scaling factor when computing the estimated standard error for δˆjn . That is, the 95% conﬁdence interval for δj is given by δˆjn ± 1.96se(δˆjn ), ˆ n )(j+1)(j+1) , where (·)q×q and se(δˆjn ) = n−1 (Σ (j+1)(j+1) denotes the (j + 1)-th diagonal element of the q × q matrix (·)q×q . The GEE estimator for β in the log-linear model, given as the solution to equation (4.11), is just one example of many possible semiparametric estimators. Clearly, we would be interested in ﬁnding a semiparametric estimator that is as eﬃcient as possible; i.e., with as small an asymptotic variance as possible. We will address this issue later in this chapter. Some natural questions that arise for semiparametric models are: (i) How do we ﬁnd semiparametric estimators, or do they even exist? (ii) How do we ﬁnd the best semiparametric estimator?

4.2 Parametric Submodels

59

Although both of these questions are diﬃcult to resolve in general, understanding the geometry of inﬂuence functions for semiparametric estimators is often helpful in constructing estimators and assessing eﬃciency. The ideas and geometry we developed in Chapter 3 for ﬁnite-dimensional parametric models will now be generalized to semiparametric models.

4.2 Parametric Submodels As is often the case in mathematics, inﬁnite-dimensional problems are tackled by ﬁrst working with a ﬁnite-dimensional problem as an approximation and then taking limits to inﬁnity. Therefore, the ﬁrst step in dealing with a semiparametric model is to consider a simpler ﬁnite-dimensional parametric model contained within the semiparametric model and use the theory and methods developed in Chapter 3. Toward that end, we deﬁne a parametric submodel. Recall: In a semiparametric model, the data Z1 , . . . , Zn are iid random vectors with a density that belongs to the class P = p{z, β, η(·)}, where β is q-dimensional and η(·) is inﬁnite-dimensional with respect to some dominating measure νZ . As we illustrated in some of the examples of semiparametric models in Chapter 1, the inﬁnite-dimensional nuisance parameter η is itself often a function and hence denoted as η(·). We will denote the “truth” (i.e., the density that generates the data) by p0 (z) ∈ P, namely p0 (z) = p{z, β0 , η0 (·)}. A parametric submodel, which we will denote by Pβ,γ = {p(z, β, γ)}, is a class of densities characterized by the ﬁnite-dimensional parameter (β T , γ T )T such that (i) Pβ,γ ⊂ P (i.e., every density in Pβ,γ belongs to the semiparametric model P) and (ii) p0 (z) ∈ Pβ,γ (i.e., the parametric submodel contains the truth). Another way of saying this is that there exists a density identiﬁed by the parameter (β0 , γ0 ) within the parametric submodel such that p0 (z) = p(z, β0 , γ0 ). In keeping with the notation of Chapter 3, we will denote the dimension of γ by “r,” although, in this case, the value r depends on the choice of parametric submodel. Remark 2. When we developed the geometry of inﬂuence functions for parametric models in Chapter 3, certain regularity conditions were implicitly assumed. For example, the parametric model had to have suﬃcient regularity

60

4 Semiparametric Models

conditions to allow the interchange of diﬀerentiation and integration of the density with respect to the parameters. This is necessary, for example, when we want to prove that the score vector has mean zero. Consequently, the parametric model has to satisfy certain smoothness conditions. Similarly, the parametric submodels that we will consider must also satisfy certain smoothness conditions. Appropriate smoothness and regularity conditions on the likelihoods are given in Deﬁnition A.1 of the appendix in Newey (1990). Thus, from here on, when we refer to parametric submodels, we implicitly are assuming smooth and regular parametric submodels. Remark 3. The terms parametric submodel and parametric model can be confusing. A parametric model is a model whose probability densities are characterized through a ﬁnite number of parameters that the data analyst believes will suﬃce in identifying the probability distribution that generates the data. For example, we may be willing to assume that our data follow the model Yi = µ(Xi , β) + εi ,

(4.15)

where εi are iid N (0, σ 2 ), independent of Xi . This model is contained within the semiparametric restricted moment model discussed previously. In contrast, a parametric submodel is a conceptual idea that is used to help us develop theory for semiparametric models. The reason we say it is conceptual is that we require a parametric submodel to contain the truth. But since we don’t know what the truth is, we can only describe such submodels generically and hence such models are not useful for data analysis. The parametric model given by (4.15) is not a parametric submodel if, in truth, the data are not normally distributed. We now illustrate how a parametric submodel can be deﬁned using the proportional hazards model as an example. In the proportional hazards model, we assume λ(t|X) = λ(t) exp(β T X), where X = (X1 , . . . , Xq )T denotes a q-dimensional vector of covariates, λ(t) is some arbitrary hazard function of time that is left unspeciﬁed and hence is inﬁnite-dimensional, and β is the q-dimensional parameter of interest. We denote (conceptually) the truth by λ0 (t); t ≥ 0 and β0 . An example of a parametric submodel is as follows. Let h1 (t), . . . , hr (t) be r diﬀerent functions of time that are speciﬁed by the data analyst. (Any smooth functions will do.) Consider the model Pβ,γ = {class of densities with hazard function λ(t|X) = λ0 (t) exp{γ1 h1 (t) + · · · + γr hr (t)} exp(β T X)}, where γ = (γ1 , . . . , γr )T ∈ Rr and β ∈ Rq .

4.3 Inﬂuence Functions for Semiparametric RAL Estimators

61

We note that: •

In this model, the (q +r) parameters (β T , γ T )T are left unspeciﬁed. Hence, this model is indeed a ﬁnite-dimensional model. • For any choice of β and γ, the resulting density follows a proportional hazards model and is therefore contained in the semiparametric model; i.e., Pβ,γ ⊂ P.

• The truth is obtained by setting β = β0 and γ = 0. • This parametric submodel is deﬁned using λ0 (t), “the truth,” which is not known to us; consequently, such a model is not useful for data analysis. Contrast this with the case where we are willing to consider the parametric model; namely λ(t|X) = λ exp(β T X), λ, β unknown. That is, we assume that the underlying baseline hazard function is constant over time; i.e., conditional on X, the survival distribution follows an exponential distribution. If we are willing to assume that our data are generated from some distribution within this parametric model, then we only need to estimate the parameters λ and β and use this for any subsequent data analysis. Of course, the disadvantage of such a parametric model is that if the data are not generated from any density within this class, then the estimates we obtain may be meaningless.

4.3 Inﬂuence Functions for Semiparametric RAL Estimators In Chapter 3, we studied RAL estimators for β for ﬁnite-dimensional parametric models and derived their asymptotic properties through their inﬂuence function. We also described the geometry of the class of inﬂuence functions for RAL estimators. Consequently, from this development, we know that inﬂuence functions of RAL estimators for β for a parametric submodel: (i) Belong to the subspace of the Hilbert space H of q-dimensional meanzero ﬁnite-variance measurable functions (equipped with the covariance inner product) that are orthogonal to the parametric submodel nuisance tangent space Λγ = {B q×r Sγ (Z, β0 , γ0 ), for all B q×r }, where Sγr×1 =

∂ log p(z, β0 , γ0 ) . ∂γ

62

4 Semiparametric Models

(ii) The eﬃcient inﬂuence function for the parametric submodel is given by T

eﬀ eﬀ −1 eﬀ ϕeﬀ Sβ,γ (Z, β0 , γ0 ), β,γ (Z) = {E(Sβ,γ Sβ,γ )} eﬀ where Sβ,γ (Z, β0 , γ0 ), the parametric submodel eﬃcient score, is

Sβ (Z, β0 , η0 ) − Π(Sβ (Z, β0 , η0 )|Λγ ), and Sβq×1 (Z, β0 , η0 ) =

∂ log p(z, β0 , η0 ) . ∂β

(iii) The smallest asymptotic variance among such RAL estimators for β in the parametric submodel is T

eﬀ eﬀ [E{Sβ,γ (Z)Sβ,γ (Z)}]−1 .

An estimator for β is an RAL estimator for a semiparametric model if it is an RAL estimator for every parametric submodel. Therefore, any inﬂuence function of an RAL estimator in a semiparametric model must be an inﬂuence function of an RAL estimator within a parametric submodel; i.e., class of inﬂuence functions class of inﬂuence functions ⊂ . of RAL estimators for β for P of RAL estimators for Pβ,γ A heuristic way of looking at this is as follows. If βˆn is a semiparametric estimator, then we want D(β,η) 1/2 ˆ n (βn − β) −−−−→ N 0, Σ(β, η) for all p(z, β, η) ∈ P. Such an estimator would necessarily satisfy D(β,γ) 1/2 ˆ n (βn − β) −−−−→ N 0, Σ(β, γ) for all p(z; β, γ) ∈ Pβ,γ ⊂ P. However, the converse may not be true. Consequently, the class of semiparametric estimators must be contained within the class of estimators for a parametric submodel. Therefore: (i) Any inﬂuence function of an RAL semiparametric estimator for β must be orthogonal to all parametric submodel nuisance tangent spaces. (ii) The variance of any RAL semiparametric inﬂuence function must be greater than or equal to T

eﬀ eﬀ [E{Sβ,γ (Z)Sβ,γ (Z)}]−1

for all parametric submodels Pβ,γ .

4.4 Semiparametric Nuisance Tangent Space

63

Hence, the variance of the inﬂuence function for any semiparametric estimator for β must be greater than or equal to / 0−1 sup eﬀ eﬀ T E Sβ,γ Sβ,γ . {all parametric submodels}

(4.16)

This supremum is deﬁned to be the semiparametric eﬃciency bound. Any semiparametric RAL estimator βˆn with asymptotic variance achieving this bound for p0 (z) = p(z, β0 , η0 ) is said to be locally eﬃcient at p0 (·). If the same estimator βˆn is semiparametric eﬃcient regardless of p0 (·) ∈ P, then we say that such an estimator is globally semiparametric eﬃcient. Geometrically, the parametric submodel eﬃcient score is the residual of Sβ (Z, β0 , η0 ) after projecting it onto the parametric submodel nuisance tangent space. For a one-dimensional parameter β, the inverse of the normsquared of this residual is the smallest variance of all inﬂuence functions for RAL estimators for the parametric submodel. This analogy can be extended to q-dimensional β as well by considering the inverse of the variance matrix of the residual q-dimensional vector. As we increase the complexity of the parametric submodel, or consider the linear space spanned by the nuisance tangent spaces of all the parametric submodels, the corresponding space becomes larger and therefore the norm of the residual becomes smaller. Hence, the inverse of the variance of the residual grows larger. This gives a geometric perspective to the observation that the eﬃcient semiparametric estimator has a variance larger than the eﬃcient estimator for any parametric submodel.

4.4 Semiparametric Nuisance Tangent Space Let us be a bit more formal. Deﬁnition 1. The nuisance tangent space for a semiparametric model, denoted by Λ, is deﬁned as the mean-square closure of parametric submodel nuisance tangent spaces, where a parametric submodel nuisance tangent space is the set of elements {B q×r Sγr×1 (Z, β0 , η0 )}, Sγ (Z, β0 , η0 ) is the score vector for the nuisance parameter γ for some parametric submodel, and B q×r is a conformable matrix with q-rows. Speciﬁcally, the mean-square closure of the spaces above is deﬁned as the space Λ ⊂ H, where Λ = [hq×1 (Z) ∈ H such that E{hT (Z)h(Z)} < ∞ and there exists a sequence Bj Sγj (Z) such that j→∞

h(Z) − Bj Sγj (Z) 2 −−−→ 0 for a sequence of parametric submodels indexed by j], where h(Z) 2 = E{hT (Z)h(Z)}.

64

4 Semiparametric Models

Remark 4. The Hilbert space H is also a metric space (i.e., a set of elements where a notion of distance between elements of the set is deﬁned). For any two elements h1 , h2 ∈ H, we can deﬁne the distance between two elements h1 , h2 ∈ H as h2 − h1 = [E{(h2 − h1 )T (h2 − h1 )}]1/2 . The closure of a set S, where, in this setting, a set consists of q-dimensional random functions with mean zero and ﬁnite variance, is deﬁned as the smallest closed set that contains S, or equivalently, as the set of all elements in S together with all the limit ¯ Thus the closure of a set is itself points of S. The closure of S is denoted by S. a closed set. Limits must be deﬁned in terms of a distance between elements. The word mean-square is used because limits are taken with respect to the distance, which in this case is the square root of the expected sum of squared diﬀerences between the q-components of the two elements (i.e., between the two q-dimensional random functions). Therefore, the mean-square closure is larger and contains the union of all parametric submodel nuisance tangent spaces. Therefore, if we denote by S the union of all parametric submodel nuisance tangent spaces, then Λ = S¯ is the semiparametric nuisance tangent space. Remark 5. Although the space Λ is closed, it may not necessarily be a linear space. However, in most applications it is a linear space, and certainly is in any of the examples used in this book. Therefore, from now on, we will always assume the space Λ is a linear space and, by construction, is also a closed space. This is important because in order for the projection theorem to be guaranteed to apply (i.e., that a unique projection of an element to a linear subspace exists), that linear subspace must be a closed linear subspace of the Hilbert space. Before deriving the semiparametric eﬃcient inﬂuence function, we ﬁrst deﬁne the semiparametric eﬃcient score vector and give some results regarding the semiparametric eﬃciency bound. Deﬁnition 2. The semiparametric eﬃcient score for β is deﬁned as Seﬀ (Z, β0 , η0 ) = Sβ (Z, β0 , η0 ) − Π{Sβ (Z, β0 , η0 )|Λ}. There is no diﬃculty with this deﬁnition since the nuisance tangent space Λ is a closed linear subspace and therefore the projection Π{Sβ (Z, β0 , η0 )|Λ} exists and is unique. Theorem 4.1. The semiparametric eﬃciency bound, deﬁned by (4.16), is equal to the inverse of the variance matrix of the semiparametric eﬃcient score; i.e., T [E{Seﬀ (Z)Seﬀ (Z)}]−1 . Proof. For simplicity, we take β to be a scalar (i.e., q = 1), although this can be extended to q > 1 using arguments in Section 3.4, where a generalization of

4.4 Semiparametric Nuisance Tangent Space

65

the Pythagorean theorem to dimension q > 1 was derived (see (3.31)). Denote by V the semiparametric eﬃciency bound, which, when q = 1, is deﬁned by eﬀ sup

Sβ,γ (Z) −2 = V, {all parametric submodels} Pβ,γ

where eﬀ (Z) = Sβ (Z) − Π(Sβ (Z)|Λγ ). Sβ,γ eﬀ Since Λγ ⊂ Λ, this implies that Seﬀ (Z) ≤ Sβ,γ (Z) for all parametric submodels Pβ,γ . Hence

Seﬀ (Z) −2 ≥

eﬀ sup Sβ,γ (Z) −2 = V. {all Pβ,γ }

(4.17)

To complete the proof of the theorem, we need to show that Seﬀ (Z) −2 is also less than or equal to V. But because Π(Sβ (Z)|Λ) ∈ Λ, this means that there exists a sequence of parametric submodels Pβ,γj with nuisance score vectors Sγj (Z) such that j→∞

Π(Sβ (Z)|Λ) − Bj Sγj (Z) 2 −−−→ 0 for conformable matrices Bj . eﬀ By the deﬁnition of V, for any Pβ,γj , we obtain V −1 ≤ Sβ,γ (Z) 2 . j Therefore eﬀ V −1 ≤ Sβ,γ (Z) 2 = Sβ (Z) − Π(Sβ (Z)|Λγj ) 2 ≤ Sβ (Z) − Bj Sγj (Z) 2 j

= Sβ (Z) − Π(Sβ (Z)|Λ) 2 + Π(Sβ (Z)|Λ) − Bj Sγj (Z) 2 .

(4.18)

Because Sβ (Z) − Π(Sβ (Z)|Λ) is orthogonal to Λ and Π(Sβ (Z)|Λ) − Bj Sγj (Z) is an element of Λ, the last equality in (4.18) follows from the Pythagorean theorem. Taking j → ∞ implies

Sβ (Z) − Π(Sβ (Z)|Λ) 2 = Seﬀ (Z) 2 ≥ V −1 or

Seﬀ (Z) −2 ≤ V. Together with (4.17), we conclude that Seﬀ (Z) −2 = V. Deﬁnition 3. The eﬃcient inﬂuence function is deﬁned as the inﬂuence function of a semiparametric RAL estimator, if it exists (see remark below), that achieves the semiparametric eﬃciency bound.

66

4 Semiparametric Models

Remark 6. In the development that follows, we will construct a unique element of H that always exists, which will be deﬁned as the eﬃcient inﬂuence function. We will prove that if a semiparametric RAL estimator exists that has an inﬂuence function whose variance is the semiparametric eﬃciency bound, then this inﬂuence function must be the eﬃcient inﬂuence function. There is no guarantee, however, that such an RAL estimator can be derived. Theorem 4.2. Any semiparametric RAL estimator for β must have an inﬂuence function ϕ(Z) that satisﬁes ' ( T (i) E{ϕ(Z)SβT (Z, β0 , η0 )} = E ϕ(Z)Seﬀ (Z, β0 , η0 ) = I q×q and (ii) Π{ϕ(Z)|Λ} = 0; i.e., ϕ(Z) is orthogonal to the nuisance tangent space. The eﬃcient inﬂuence function is now deﬁned as the unique element satisfying conditions (i) and (ii) whose variance matrix equals the eﬃciency bound and is equal to (−1 ' T ϕeﬀ (Z, β0 , η0 ) = E Seﬀ Seﬀ Seﬀ (Z, β0 , η0 ). Proof. We ﬁrst prove condition (ii). To show that ϕ(Z) is orthogonal to Λ, we must prove that ϕ, h = 0 for all h ∈ Λ. By the deﬁnition of Λ, there exists a sequence Bj Sγj (Z) such that j→∞

h(Z) − Bj Sγj (Z) −−−→ 0 for a sequence of parametric submodels indexed by j. Hence

ϕ, h = ϕ, Bj Sγj + ϕ, h − Bj Sγj . Because any inﬂuence function of a semiparametric RAL estimator for β must be an inﬂuence function for an RAL estimator in a parametric submodel, then condition (ii) of Corollary 1 of Theorem 3.2 implies that ϕ is orthogonal to Λγj and hence the ﬁrst term in the sum above is equal to zero. By the CauchySchwartz inequality, we obtain | ϕ, h| ≤ ϕ

h − Bj Sγj . Taking limits as j → ∞ gives us the desired result. To prove condition (i) above, we note that by condition (i) of Corollary 1 of Theorem 3.2, ϕ(Z) must satisfy E{ϕ(Z)SβT (Z, β0 , η0 )} = I q×q . Since T E{ϕ(Z)Seﬀ (Z, β0 , η0 )} = E{ϕ(Z)SβT (Z, β0 , η0 )}−E{ϕ(Z)Π(SβT (Z, β0 , η0 )|Λ)}, the result follows because Π(SβT (Z, β0 , η0 )|Λ) ∈ Λ and since, by condition (ii), ϕ(Z) is orthogonal to Λ, this implies that E{ϕ(Z)Π(SβT (Z, β0 , η0 )|Λ)} = 0. It is now easy to show that the eﬃcient inﬂuence function is given by (−1 ' T ϕeﬀ (Z, β0 , η0 ) = E Seﬀ Seﬀ Seﬀ (Z, β0 , η0 ).

4.4 Semiparametric Nuisance Tangent Space

67

Clearly, ϕeﬀ (Z, β0 , η0 ) satisﬁes conditions (i) and (ii) above and moreover has variance matrix E{ϕeﬀ (Z)ϕTeﬀ (Z)} = V, where V is the semiparametric eﬃciency bound. The results of Theorem 4.2 apply speciﬁcally to semiparametric models that can be parametrized through (β, η), where β is the parameter of interest, η is the inﬁnite-dimensional nuisance parameter, and for which the nuisance tangent space Λ can be readily computed. For some semiparametric models, it may be more convenient to parametrize the model through an inﬁnitedimensional parameter θ for which the tangent space can be readily computed and deﬁne the parameter of interest as β(θ); i.e., a smooth q-dimensional function of θ. For parametric models, we showed in Chapter 3, Theorem 3.4, that the space of inﬂuence functions for RAL estimators for β lies on a linear variety deﬁned as ϕ(Z) + Tθ⊥ , where ϕ(Z) is the inﬂuence function of any RAL estimator for β and Tθ is the parametric model tangent space, the space spanned by the score vector Sθ (Z). Similarly, for semiparametric models, we could show that the inﬂuence functions of semiparametric RAL estimators for β must lie on the linear variety ϕ(Z) + T ⊥ , where ϕ(Z) is the inﬂuence function of any semiparametric RAL estimator for β and T is the semiparametric tangent space, deﬁned as the mean-square closure of all parametric submodel tangent spaces. The element in this linear variety with the smallest norm is the unique element ϕ(Z) − Π{ϕ(Z)|T ⊥ } = Π{ϕ(Z)|T }, which can be shown to be the eﬃcient inﬂuence function ϕeﬀ (Z). We give these results in the following theorem, whose proof is analogous to that in Theorems 3.4 and 3.5 and is therefore omitted. Theorem 4.3. If a semiparametric RAL estimator for β exists, then the inﬂuence function of this belong to the space of inﬂuence functions, estimator must the linear variety

ϕ(Z) + T ⊥ , where ϕ(Z) is the inﬂuence function of any

semiparametric RAL estimator for β and T is the semiparametric tangent space, and if an RAL estimator for β exists that achieves the semiparametric eﬃciency bound (i.e., a semiparametric eﬃcient estimator), then the inﬂuence function of this estimator must be the unique and well-deﬁned element ϕeﬀ (Z) = ϕ(Z) − Π{ϕ(Z)|T ⊥ } = Π{ϕ(Z)|T }. What is not clear is whether there exist semiparametric estimators that will have inﬂuence functions corresponding to the elements of the Hilbert space satisfying conditions (i) and (ii) of Theorem 4.2 or Theorem 4.3 (although we might expect that arguments similar to those in Section 3.3, used to construct estimators for ﬁnite-dimensional parametric models, will extend to semiparametric models as well). In many cases, deriving the space of inﬂuence functions, or even the space orthogonal to the nuisance tangent space, for semiparametric models, will

68

4 Semiparametric Models

suggest how semiparametric estimators may be constructed and even how to ﬁnd locally or globally eﬃcient semiparametric estimators. We will illustrate this for the semiparametric restricted moment model. But before doing so, it will be instructive to develop some methods and tools for ﬁnding inﬁnitedimensional tangent spaces. We start with the nonparametric model, where we put no restrictions on the class of densities and show that the corresponding tangent space is the entire Hilbert space H. Tangent Space for Nonparametric Models Suppose we are interested in estimating some q-dimensional parameter β for a nonparametric model. That is, let Z1 , . . . , Zn be iid random vectors with arbitrary density p(z) with respect to a dominating measure νZ , where the only restriction on p(z) is that p(z) ≥ 0 and p(z)dν(z) = 1. Theorem 4.4. The tangent space (i.e., the mean-square closure of all parametric submodel tangent spaces) is the entire Hilbert space H. Proof. Consider any parametric submodel Pθ = {p(z, θ), θ, say s-dimensional}. The parametric submodel tangent space is ( ' Λθ = B q×s Sθ (Z), for all constant matrices B q×s , where Sθ (z) =

∂ log p(z, θ0 ) . ∂θ

Denote the truth as p0 (z) = p(z, θ0 ). From the usual properties of score vectors, we know that E{Sθ (Z)} = 0s×1 . Consequently, the linear subspace Λθ ⊂ H. Reminder: When we write E{Sθ (Z)}, we implicitly mean that the expectation is computed with respect to the truth; i.e., E0 {Sθ (Z, θ0 )} or Eθ0 {Sθ (Z, θ0 )}. To complete the proof, we need to show that any element of H can be written as an element of Λθ for some parametric submodel or a limit of such elements. Choose an arbitrary element of H, say h(Z) that is a bounded meanzero q-dimensional measurable function with ﬁnite variance. Consider the parametric submodel p(z, θ) = p0 (z){1 + θT h(z)}, where θ is a q-dimensional vector suﬃciently small so that

4.4 Semiparametric Nuisance Tangent Space

{1 + θT h(z)} ≥ 0

for all z.

69

(4.19)

Condition (4.19) is necessary to guarantee that p(z, θ) is a proper density. Because h(·) is a bounded function, the set of θ satisfying (4.19) contains an open set in Rq . This must be the case in order to ensure that the partial derivatives of p(z, θ) with respect to θ exist. Moreover, every element p(z, θ) in the parametric submodel satisﬁes ' ( p(z, θ)dν(z) = p0 (z) 1 + θT h(z) dν(z) = p0 (z)dν(z) + θT h(z)p0 (z)dν(z) = 1. * +, * +,

1

0

This guarantees that p(z, θ), for θ in some neighborhood of the truth, is a proper density function. For this parametric submodel, the score vector is Sθ (z) =

∂ log[p0 (z){1 + θT h(z)}] ∂θ

θ=0

= h(z). If we choose B q×q to be I q×q (i.e., the q ×q identity matrix), then h(Z), which also equals I q×q h(Z), is an element of this parametric submodel tangent space. Thus we have shown that the tangent space contains all bounded meanzero random vectors. The proof is completed by noting that any element of H can be approximated by a sequence of bounded h. Remark 7. On the dimension of θ When we deﬁned an arbitrary parametric submodel through the parameter θ, the dimension of θ was taken to be s-dimensional, where s was arbitrary. The corresponding score vector, which also is s-dimensional, had to be premultiplied by some arbitrary constant matrix B q×s to obtain an element in the Hilbert space. However, when we were constructing a parametric submodel that led to the score vector h(Z), we chose θ to be q-dimensional to conform to the dimension of h(Z); i.e., for this particular parametric submodel, we took s to equal q.

Partitioning the Hilbert Space Suppose Z is an m-dimensional random vector, say Z = Z (1) , . . . , Z (m) . Then the density of Z can be expressed as the product of conditional densities, namely

70

4 Semiparametric Models

pZ (z) =pZ (1) (z (1) ) × pZ (2) |Z (1) (z (2) |z (1) ) × . . . × pZ (m) |Z (1) ,...,Z (m−1) (z (m) |z (1) , . . . , z (m−1) ), where

pZ (j) |Z (1) ,...,Z (j−1) (z (j) |z (1) , . . . , z (j−1) )

(4.20)

is the conditional density of Z (j) given Z (1) , . . . , Z (j−1) , deﬁned with respect to the dominating measure νj . If we put no restrictions on the density of Z (i.e., the nonparametric model) or, equivalently, put no restrictions on the conditional densities above, then the j-th conditional density (4.20) is any positive function ηj (z (1) , . . . , z (j) ) such that ηj (z (1) , . . . , z (j) )dνj (z (j) ) = 1 for all z (1) , . . . , z (j−1) , j = 1, . . . , m. With this representation, we note that the nonparametric model can be represented by the m variationally independent inﬁnite-dimensional nuisance parameters η1 (·), . . . , ηm (·). By variationally independent, we mean that the product of any combination of arbitrary η1 (·), . . . , ηm (·) can be used to construct a valid density for Z (1) , . . . , Z (m) in the nonparametric model. The tangent space T is the mean-square closure of all parametric submodel tangent spaces, where a parametric submodel is given by the class of densities m ! p(z (1) , γ1 ) p(z (j) |z (1) , . . . , z (j−1) , γj ), j=2

γj , j = 1, . . . , m are sj -dimensional parameters that are variationally independent, and p(z (j) |z (1) , . . . , z (j−1) , γ0j ) denotes the true conditional density of Z (j) given Z (1) , . . . , Z (j−1) . The parametric submodel tangent space is deﬁned as the space spanned by the score vectors Sγj (Z (1) , . . . , Z (m) ), j = 1, . . . , m, where Sγj (z (1) , . . . , z (m) ) = ∂ log p(z (1) , . . . , z (m) , γ1 , . . . , γm )/∂γj . Because the density of Z (1) , . . . , Z (m) is a product of conditional densities, each parametrized through parameters γj that are variationally independent, this implies that the log-density is a sum of log-conditional densities with respect to variationally independent parameters γj . Hence, Sγj (z (1) , . . . , z (m) ), which is deﬁned as ∂ log p(z (j) |z (1) , . . . , z (j−1) , γj )/∂γj , is a function of z (1) , . . . , z (j) only. Moreover, the parametric submodel tangent space can be written as the space q×sm Tγ = B1q×s1 Sγ1 (Z (1) ) + . . . + Bm Sγm (Z (1) , . . . , Z (m) ) q×sm for all constant matrices B1q×s1 , . . . , Bm . Consequently, the parametric submodel tangent space is equal to

Tγ = Tγ1 ⊕ . . . ⊕ Tγm , where

4.4 Semiparametric Nuisance Tangent Space

71

Tγj = {B q×sj Sγj (Z (1) , . . . , Z (j) ), for all constant matrices B q×sj }. It is now easy to verify that the tangent space T , the mean-square closure of all parametric submodel tangent spaces, is equal to T = T1 ⊕ . . . ⊕ Tm , where Tj , j = 1, . . . , m, is the mean-square closure of parametric submodel tangent spaces for ηj (·), where a parametric submodel for ηj (·) is given by the class of conditional densities Pγj = {p(z (j) |z (1) , . . . , z (j−1) , γj ), γj − say sj − dimensional} and the parametric submodel tangent space Tγj is the linear space spanned by the score vector Sγj (Z (1) , . . . , Z (j) ). We now are in a position to derive the following results regarding the partition of the Hilbert space H into a direct sum of orthogonal subspaces. Theorem 4.5. The tangent space T for the nonparametric model, which we showed in Theorem 4.4 is the entire Hilbert space H, is equal to T = H = T1 ⊕ . . . ⊕ Tm , where

T1 = α1q×1 (Z (1) ) ∈ H : E{α1q×1 (Z (1) )} = 0q×1

and

Tj = αjq×1 (Z (1) , . . . , Z (j) ) ∈ H :

(4.21)

E{αjq×1 (Z (1) , . . . , Z (j) )|Z (1) , . . . , Z (j−1) } = 0q×1 , j = 2, . . . , m, and Tj , j = 1, . . . , m are mutually orthogonal spaces. Equivalently, the linear space Tj can be deﬁned as the space (1) (1) [hq×1 , . . . , Z (j) ) − E{hq×1 , . . . , Z (j) )|Z (1) , . . . , Z (j−1) }] (4.22) ∗j (Z ∗j (Z (1) , . . . , Z (j) . for all square-integrable functions hq×1 ∗j (·) of Z (1) (m) In addition, any element h(Z , . . . , Z ) ∈ H can be decomposed into orthogonal elements h = h 1 + . . . + hm ,

where h1 (Z (1) ) = E{h(·)|Z (1) }, hj (Z

(1)

, . . . , Z (j) ) = E{h(·)|Z (1) , . . . , Z (j) } − E{h(·)|Z (1) , . . . , Z (j−1) },

(4.23)

for j = 2, . . . , m, and hj (·) is the projection of h onto Tj ; i.e., hj (·) = Π{h(·)|Tj }.

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4 Semiparametric Models

Proof. That the partition of the tangent space Tj associated with the nuisance parameter ηj (·) is the set of elements given by (4.21) follows by arguments similar to those for the proof of Theorem 4.4. That is, because of properties of score functions for parametric models of conditional densities, the score vector Sγj (·) must be a function only of Z (1) , . . . , Z (j) and must have conditional expectation E{Sγj (Z (1) , . . . , Z (j) )|Z (1) , . . . , Z (j−1) } = 0sj ×1 . Consequently, any q-dimensional element spanned by Sγj (·) must belong to Tj . Conversely, for any bounded element αj (Z (1) , . . . , Z (j) ) in Tj , consider the parametric submodel pj (z (j) |z (1) , . . . , z (j−1) , θj ) = p0j (z (j) |z (1) , . . . , z (j−1) ){1+θjT αj (z (1) , . . . , z (j) )}, (4.24) where p0j (z (j) |z (1) , . . . , z (j−1) ) denotes the true conditional density of Z (j) given Z (1) , . . . , Z (j−1) and θj is a q-dimensional parameter chosen suﬃciently small to guarantee that pj (z (j) |z (1) , . . . , z (j−1) , θj ) is positive. This class of functions is clearly a parametric submodel since p0j (z (j) |z (1) , . . . , z (j−1) ){1 + θjT αj (z (1) , . . . , z (j) )}dνj (z (j) ) = 1, which follows because p0j (z (j) |z (1) , . . . , z (j−1) )dνj (z (j) ) = 1, p0j (z (j) |z (1) , . . . , z (j−1) ){θjT αj (z (1) , . . . , z (j) )}dνj (z (j) ) = 0, (4.25) where (4.25) is equal to θjT E{αj (Z (1) , . . . , Z (j) )|Z (1) , . . . , Z (j−1) }, which must equal zero by the deﬁnition of Tj . The score vector for the parametric submodel (4.24) is Sθj (·) =

∂ log p0j (z (j) |z (1) , . . . , z (j−1) ){1 + θjT αj (z (1) , . . . , z (j) )} ∂θj

= αj (z

(1)

,...,z

(j)

θj =0

).

Thus we have shown that the tangent space for every parametric submodel of ηj (·) is contained in Tj and every bounded element in Tj belongs to the tangent space for some parametric submodel of ηj (·). The argument is completed by noting that every element of Tj is the limit of bounded elements of Tj . That the projection of any element h ∈ H onto Tj is given by hj (·), deﬁned by (4.23), can be veriﬁed directly. Clearly hj (·) ∈ Tj . Therefore, by the projection theorem for Hilbert spaces, we only need to verify that h − hj is orthogonal to every element of Tj . Consider any arbitrary element j ∈ Tj .

4.5 Semiparametric Restricted Moment Model

73

Then, because hj and lj are functions of Z (1) , . . . , Z (j) , we can use the law of iterated conditional expectations to obtain E{(h − hj )T j } = E[E{(h − hj )T j |Z (1) , . . . , Z (j) }] = E[{E(h|Z (1) , . . . , Z (j) ) − hj }T j ] = E[{E(h|Z (1) , . . . , Z (j−1) )}T j ] / 0 = E E[{E(h|Z (1) , . . . , Z (j−1) )}T j |Z (1) , . . . , Z (j−1) ]

(4.26)

= E[{E(h|Z (1) , . . . , Z (j−1) )}T E(j |Z (1) , . . . , Z (j−1) )] = 0.

(4.27)

Note that (4.26) follows from the deﬁnition of hj . The equality in (4.27) follows because lj ∈ Tj , which, in turn, implies that E(lj |Z (1) , . . . , Z (j−1) ) = 0. Finally, in order to prove that Tj , j = 1, . . . , m are mutually orthogonal subspaces, we must show that hj is orthogonal to hj , where hj ∈ Tj , hj ∈ Tj and j = j , j, j = 1, . . . , m. This follows using the law of iterated conditional expectations, which we leave for the reader to verify.

4.5 Semiparametric Restricted Moment Model We will focus a great deal of attention on studying the geometric properties of inﬂuence functions of estimators for parameters in the restricted moment model because of its widespread use in statistical modeling. The relationship of the response variable Y , which may be univariate or multivariate, and covariates X is modeled by considering the conditional expectation of Y given X as a function of X and a ﬁnite number of parameters β. This includes linear as well as nonlinear models. For example, if Y is a univariate response variable, then, in a linear model, we would assume that E(Y |X) = X T β, where the dimensions of X and β were, say, equal to q. We also gave an example of a log-linear model earlier, in Section 4.1. More generally, the response variable Y may be multivariate, say d-dimensional, and the relationship of the conditional expectation of Y given X may be linear or nonlinear, say E(Y |X) = µ(X, β), where µ(x, β) is a d-dimensional function of the covariates X and the q-dimensional parameter β. Therefore, such models are particularly useful when modeling longitudinal and/or multivariate response data. The goal is to estimate the parameter β using a sample of iid data (Yi , Xi ), i = 1, . . . , n. As we argued in Chapter 1, without any additional restrictions on the joint probability distribution of Y and X, this is a semiparametric model. The restricted moment model can also be expressed as Y = µ(X, β) + ε, where E(ε|X) = 0.

74

4 Semiparametric Models

We will assume, for the time being, that the d-dimensional response variable Y is continuous; i.e., the dominating measure is the Lebesgue measure, which we will denote by Y . It will be shown later how this can be generalized to more general dominating measures that will also allow Y to be discrete. The covariates X may be continuous, discrete, or mixed, and we will denote the dominating measure by νX . The observed data are assumed to be realizations of the iid random vectors (Z1 , . . . , Zn ), where Zi = (Yi , Xi ). Our aim is to ﬁnd semiparametric estimators for β and identify, if possible, the most eﬃcient semiparametric estimator. The density of a single observation, denoted by p(z), belongs to the semiparametric model P=

p{z, β, η(·)}, z = (y, x) ,

deﬁned with respect to the dominating measure Y × νX . The truth (i.e., the density that generates the data) is denoted by p0 (z) = p{z, β0 , η0 (·)}. Because there is a one-to-one transformation of (Y, X) and (ε, X), we can express the density pY,X (y, x) = pε,X {y − µ(x, β), x}, (4.28) where pε,X (ε, x) is a density with respect to the dominating measure ε × νX . The restricted moment model only makes the assumption that E(ε|X) = 0. As illustrated in Example 1 of Section 1.2, the density of (ε, X) can be expressed as pε,X (ε, x) = η1 (ε, x) η2 (x), where η1 (ε, x) = pε|X (ε|x) is any nonnegative function such that η1 (ε, x)dε = 1 for all x, εη1 (ε, x)dε = 0 for all x, and pX (x) = η2 (x) is a nonnegative function of x such that η2 (x)dν(x) = 1.

(4.29)

(4.30) (4.31)

(4.32)

The set of functions η1 (ε, x) and η2 (x), satisfying the constraints (4.30), (4.31), and (4.32), are inﬁnite-dimensional and can be used to characterize the semiparametric model as p{z, β, η1 (·), η2 (·)} = η1 {y − µ(x, β), x}η2 (x).

4.5 Semiparametric Restricted Moment Model

75

The true density generating the data is denoted by p0 (z) = η10 {y − µ (x, β0 ), x} η20 (x) = p{z, β0 , η10 (·), η20 (·)}. To develop the semiparametric theory and deﬁne the semiparametric nuisance tangent space, we ﬁrst consider parametric submodels. Instead of arbitrary functions η1 (ε, x) and η2 (x) for pε|X (ε|x) and pX (x) satisfying constraints (4.30), (4.31), and (4.32), we will consider parametric submodels pε|X (ε|x, γ1 )

and pX (x, γ2 ),

where γ1 is an r1 -dimensional vector and γ2 is an r2 -dimensional vector. Thus γ = (γ1T , γ2T )T is an r-dimensional vector, r = r1 + r2 . This parametric submodel is given as Pβ,γ = {p(z, β, γ1 , γ2 ) = pε|X {y − µ(x, β)|x, γ1 }pX (x, γ2 )

(4.33)

for

β T , γ1T , γ2T

T

∈ Ωβ,γ ⊂ Rq+r }.

Also, to be a parametric submodel, Pβ,γ must contain the truth; i.e., p0 (z) = pε|X {y − µ(x, β0 )|x, γ10 }pX (x, γ20 ). We begin by deﬁning the parametric submodel nuisance tangent space and will show how this leads us to the semiparametric model nuisance tangent space. The parametric submodel nuisance score vector is given as T T "T ∂ log p(z, β, γ) Sγ (z, β0 , γ0 ) = , β = β0 , ∂γ2 γ = γ0 ' T ( T = Sγ1 (z, β0 , γ0 ), SγT2 (z, β0 , γ0 ) .

∂ log p(z, β, γ) ∂γ1

By (4.33), we note that log p(z, β, γ1 , γ2 ) = log pε|X {y − µ(x, β)|x, γ1 } + log pX (x, γ2 ). Therefore, Sγ1 (z, β0 , γ0 ) = and

∂ log pε|X {y − µ(x, β0 )|x, γ10 } , ∂γ1

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4 Semiparametric Models

Sγ2 (z, β0 , γ0 ) =

∂ log pX (x, γ20 ) . ∂γ2

Since we are taking derivatives with respect to γ1 and γ2 and leaving β ﬁxed for the time being at “β0 ,” we will use the simplifying notation that ε = y − µ(x, β0 ). Also, unless stated otherwise, if parameters are omitted in an expression, they will be understood to be evaluated at the truth. So, for example, Sγ1 (z, β0 , γ0 ) =

∂ log pε|X {y − µ(x, β0 )|x, γ10 } ∂γ1

will be denoted as Sγ1 (ε, x). A typical element in the parametric submodel nuisance tangent space is given by q×r1 q×r2 q×r *B+, - Sγ (ε, X) = B1 Sγ1 (ε, X) + B2 Sγ2 (X). matrix of constants

Therefore, the parametric submodel nuisance tangent space ( ' Λγ = B q×r Sγ for all B q×r can be written as the direct sum Λγ1 ⊕ Λγ2 , where ( ' Λγ1 = B q×r1 Sγ1 (ε, X) for all B q×r1

(4.34)

and ' Λγ2 = B q×r2 Sγ2 (X)

( for all B q×r2 .

(4.35)

It is easy to show that the space Λγ1 is orthogonal to the space Λγ2 , as we demonstrate in the following lemma. Lemma 4.1. The space Λγ1 deﬁned by (4.34) is orthogonal to the space Λγ2 deﬁned by (4.35). Proof. Since pε|X (ε|x, γ1 ) is a conditional density, then by properties of score vectors E{Sγ1 (ε, X)|X} = 0. (4.36) (Equation (4.36) is also derived explicitly later in (4.37).) Similarly, E{Sγ2 (X)} = 0. Consequently,

4.5 Semiparametric Restricted Moment Model

77

E{Sγ1 (ε, X)SγT2 (X)} = E E{Sγ1 (ε, X)SγT2 (X)|X} T = E E{Sγ1 (ε, X)|X}Sγ2 (X) = 0r1 ×r2 . * +,

0 Convince yourself that (4.36) suﬃces to show that every element of Λγ1 is orthogonal to every element of Λγ2 . By deﬁnition, the semiparametric nuisance tangent space mean-square closure of all parametric Λ= submodel nuisance tangent spaces = {mean-square closure of Λγ1 ⊕ Λγ2 }. Because γ1 , γ2 are variationally independent – that is, proper densities in the parametric submodel can be deﬁned by considering any combination of γ1 and γ2 – this implies that Λ = Λ1s ⊕ Λ2s , where Λ1s = {mean-square closure of all Λγ1 }, Λ2s = {mean-square closure of all Λγ2 }. We now show how to explicitly derive the spaces Λ1s , Λ2s and the space orthogonal to the nuisance tangent space Λ⊥ . The Space Λ2s Since here we are considering marginal distributions of X with no restrictions, ﬁnding the space Λ2s is similar to ﬁnding the nuisance tangent space for the nonparametric model given in Section 4.4. For completeness, we give the arguments so that the reader can become more facile with the techniques used in such exercises. Theorem 4.6. The space Λ2s consists of all q-dimensional mean-zero functions of X with ﬁnite variance. Remark 8. In many cases, the structure of the parametric submodel nuisance tangent space will allow us to make an educated guess for the semiparametric nuisance tangent space; i.e., the mean-square closure of parametric submodel nuisance tangent spaces. After we make such a guess, we then need to verify that our guess is correct. We illustrate below. Proof. For any parametric submodel,

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4 Semiparametric Models

(i) Sγ2 (Z) = Sγ2 (X) is a function only of X and (ii) Any score vector has mean zero E{Sγ2 (X)} = 0. Therefore, any element of Λγ2 = {B q×r2 Sγ2 (X) for all B} is a q-dimensional function of X with mean zero. It may be reasonable to guess that Λ2s , the mean-square closure of all Λγ2 , is the linear subspace of all q-dimensional mean-zero functions of X. Recall: The Hilbert space is made up of all q-dimensional mean-zero functions of Z = (Y, X); hence, the conjectured space Λ2s ⊂ H. We denote the conjectured space as (conj)

Λ2s

= {all q-dimensional mean-zero functions of X}.

In order to verify that our conjecture is true, we must demonstrate: (conj)

(a) Any element of Λγ2 , for any parametric submodel, belongs to Λ2s , and conversely, (conj) is either an element of Λγ2 for some parametric (b) any element of Λ2s submodel or a limit of such elements. (a) is true because E{B q×r2 Sγ2 (X)} = 0q×1 . (conj)

To verify (b), we start by choosing a bounded element α(X) ∈ Λ2s

; i.e.,

E{αq×1 (X)} = 0q×1 , α(x)p0 (x)dν(x) = 0.

Consider the parametric submodel with density pX (x, γ2 ) = p0 (x){1 + γ2T α(x)}, γ2 is a q-dimensional vector, and γ2 is taken suﬃciently small so that {1 + γ2T α(x)} ≥ 0 for all x. This is necessary to guarantee that pX (x, γ2 ) is a proper density in a neighborhood of γ2 around zero and is true because α(x) is bounded. The function pX (x, γ2 ), in x, is a density function since pX (x, γ2 ) ≥ 0 for all x and

4.5 Semiparametric Restricted Moment Model

79

p0 (x){1 + γ2T α(x)}dν(x) = p0 (x)dν(x) + γ2T α(x)p0 (x) dν(x) = 1. * +, - * +, -

pX (x, γ2 )dν(x) =

1

0

For this parametric submodel, the score vector is Sγ2 (x) =

∂ log p0 (x){1 + γ2T α(x)} ∂γ2

γ2 =0

= α(X). Hence, by choosing the constant matrix B q×q to be the q × q identity matrix, we deduce that α(X) is an element of this particular parametric submodel (conj) nuisance tangent space. Since arbitrary α(X) ∈ Λ2s can always be taken as limits of bounded mean-zero functions of X, we have thus shown that all (conj) are either elements of a parametric submodel nuisance elements of Λ2s tangent space or a limit of such elements; hence, our conjecture has been veriﬁed and Λ2s = {all q-dimensional mean-zero functions of X}. The Space Λ1s Theorem 4.7. The space Λ1s is the space of all q-dimensional random functions a(ε, x) that satisfy (i) E{a(ε, X)|X} = 0q×1 and (ii) E{a(ε, X)εT |X} = 0q×d . Proof. The space Λ1s is the mean-square closure of all parametric submodel nuisance tangent spaces Λγ1 , where Λγ1 = {B q×r1 Sγ1 (ε, X) for all B q×r1 } and Sγ1 (ε, x) =

∂ log pε|X (ε|x, γ10 ) . ∂γ1

Recall: We use ε to denote {y − µ(x, β0 )}. Note the following relationships:

80

4 Semiparametric Models

∂ pε|X (ε|x, γ1 )dε = 0 for ∂γ1 all x and γ1 . Interchanging integration and diﬀerentiation, dividing and multiplying by pε|X (ε|x, γ10 ), and evaluating at γ1 = γ10 , we obtain

(i)

pε|X (ε|x, γ1 )dε = 1 for all x, γ1 , implies

∂pε|X (ε|x, γ10 )/∂γ1 pε|X (ε|x, γ10 )dε = 0 pε|X (ε|x, γ10 )

for all x; i.e., E{Sγ1 (ε, X)|X} = 0.

(4.37)

(ii) The model restriction E(ε|X) = 0 is equivalent to

pε|X (ε|x, γ1 )εT dε =

01×d for all x, γ1 . Using arguments similar to (i), where we diﬀerentiate with respect to γ1 , interchange integration and diﬀerentiation, divide and multiply by pε|X (ε|x, γ10 ), and set γ1 to γ10 , we obtain Sγ1 (ε, x)εT pε|X (ε|x, γ10 )dε = 0 for all x; i.e., E{Sγ1 (ε, X)εT |X} = 0r1 ×d . Consequently, any element of Λγ1 = B q×r1 Sγ1 (ε, X), say a(ε, X), must satisfy (i) E{a(ε, X)|X} = 0

(4.38)

(ii) E{a(ε, X)εT |X} = 0q×d .

(4.39)

and

A reasonable conjecture is that Λ1s is the space of all q-dimensional functions of (ε, X) that satisfy (4.38) and (4.39). To verify this, consider the parametric submodel pε|X (ε|x, γ1 ) = p0ε|X (ε|x){1 + γ1T a(ε, x)} for some bounded function a(ε, X) satisfying (4.38) and (4.39) and a qdimensional parameter γ1 chosen suﬃciently small so that {1 + γ1T a(ε, x)} ≥ 0 for all ε, x. This parametric submodel contains the truth; i.e., (γ1 = 0). Also, the class of densities in this submodel consists of proper densities, pε|X (ε|x, γ1 )dε = 1 for all x, γ1 , and E(ε|X) = 0,

4.5 Semiparametric Restricted Moment Model

81

εpε|X (ε|x, γ1 )dε = 0d×1 for all x, γ1 . For this parametric submodel, the score vector is Sγ1 (ε, x) =

∂ log pε|X (ε|x, γ10 ) = a(ε, x). ∂γ1

Premultiplying this score vector by the q × q identity matrix leads us to the conclusion that a(ε, x) is an element of this parametric submodel nuisance tangent space. Finally, any a(ε, X) ∈ H satisfying (4.38) and (4.39) can be obtained, in the limit, by a sequence of bounded a(ε, X) satisfying (4.38) and (4.39). Recap: Λ1s = {aq×1 (ε, X) such that E{a(ε, X)|X} = 0 and E{a(ε, X)εT |X} = 0}, Λ2s = {αq×1 (X) such that E{α(X)} = 0}. It is now easy to demonstrate that Λ1s is orthogonal to Λ2s . Lemma 4.2. Λ1s ⊥ Λ2s Proof. E{αT (X)a(ε, X)} = E[E{αT (X)a(ε, X)|X}] = E[αT (X)E {a(ε, X)|X}] = 0. * +,

0 The nuisance tangent space for the semiparametric model is Λ = Λ1s ⊕Λ2s . Note that Λ1s is the intersection of two linear subspaces; namely, q×1 q×1 , Λ1sa = aa (ε, X) : E{aa (ε, X)|X} = 0 and

Λ1sb =

aq×1 (ε, X) b

:

E{aq×1 b

(ε, X)ε |X} = 0 T

q×d

.

Therefore, the nuisance tangent space Λ = Λ2s ⊕ (Λ1sa ∩ Λ1sb ). There are certain relationships between the spaces Λ2s , Λ1sa , and Λ1sb that will allow us to simplify the representation of the nuisance tangent space Λ and also allow us to derive the space orthogonal to the nuisance tangent space Λ⊥ more easily. We give these relationships through a series of lemmas.

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4 Semiparametric Models

Lemma 4.3.

Λ1sa = Λ⊥ 2s .

Lemma 4.4. Λ2s ⊂ Λ1sb . and Lemma 4.5. Λ = Λ2s ⊕ (Λ1sa ∩ Λ1sb ) = Λ1sb . Proof. Lemma 4.3 We ﬁrst show that the space Λ1sa is orthogonal to Λ2s . Let a(ε, X) be an arbitrary element of Λ1sa and α(X) be an arbitrary element of Λ2s . Then E{αT (X)a(ε, X)} = E[E{αT (X)a(ε, X)|X}] = E[αT (X)E {a(ε, X)|X}] = 0. +, *

0 To complete the proof, we must show that any element h ∈ H can be written as h = h1 ⊕ h2 , where h1 ∈ Λ2s and h2 ∈ Λ1sa . We write h = E(h|X) + {h − E(h|X)}. That E(h|X) ∈ Λ2s and {h − E(h|X)} ∈ Λ1sa follow immediately. The result above also implies that Π(h|Λ2s ) = E(h|X) and Π(h|Λ1sa ) = {h − E(h|X)}. Proof. Lemma 4.4 Consider any element α(X) ∈ Λ2s . Then E{α(X)εT |X} = α(X)E(εT |X) = 0q×d , which follows from the model restriction E(εT |X) = 01×d . Hence α(X) ∈ Λ1sb . Proof. Lemma 4.5 Consider any element h1 ∈ Λ2s . By Lemma 4.4, h1 ∈ Λ1sb . Let h2 be any element in (Λ1sa ∩ Λ1sb ). Then, by deﬁnition, h2 ∈ Λ1sb . Hence (h1 + h2 ) ∈ Λ1sb since Λ1sb is a linear space. Conversely, let h be any element of Λ1sb . Since by Lemmas 4.3 and 4.4 E(h|X) ∈ Λ2s ⊂ Λ1sb , this implies that {h − E(h|X)} ∈ Λ1sb since Λ1sb is a linear space. Therefore h can be written as E(h|X) + {h − E(h|X)}, where E(h|X) ∈ Λ2s and {h − E(h|X)} ∈ Λ1sb . But by Lemma 4.3, {h − E(h|X)} is also an element of Λ1sa and hence {h − E(h|X)} ∈ (Λ1sa ∩ Λ1sb ), completing the proof.

4.5 Semiparametric Restricted Moment Model

83

Consequently, we have shown that the nuisance tangent space Λ for the semiparametric restricted moment model is given by Λ = Λ1sb = {h(ε, X) such that E{h(ε, X)εT |X} = 0q×d }.

(4.40)

Inﬂuence Functions and the Eﬃcient Inﬂuence Function for the Restricted Moment Model The key to deriving the space of inﬂuence functions is ﬁrst to identify elements of the Hilbert space that are orthogonal to Λ. Equivalently, the space Λ⊥ is the linear space of residuals h(ε, X) − Π(h(ε, X)|Λ) for all h(ε, X) ∈ H. Using (4.40), this equals [h(ε, X) − {Π(h|Λ1sb )}] .

(4.41)

Theorem 4.8. The space orthogonal to the nuisance tangent space, Λ⊥ , or equivalently Λ⊥ 1sb , is {Aq×d (X)ε for all Aq×d (X)},

(4.42)

where Aq×d (X) is the matrix of arbitrary q × d-dimensional functions of X. Moreover, the projection of any arbitrary element h(ε, X) ∈ H onto Λ1sb satisﬁes h(ε, X) − Π(h|Λ1sb ) = g q×d (X)ε, (4.43) where

g(X) = E{h(ε, X)εT |X} {E(εεT |X)}−1 ,

which implies that Π[h|Λ1sb ] = h − E(hεT |X){E(εεT |X)}−1 ε.

(4.44)

Proof. Theorem 4.8 In order to prove that the space given by (4.42) is the orthogonal complement of Λ1sb , we ﬁrst prove that this space is orthogonal to Λ1sb . That is, for any A(X)ε, we must show E{aTb (ε, X)A(X)ε} = 0 for all ab ∈ Λ1sb . By a conditioning argument, this expectation equals

(4.45)

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4 Semiparametric Models

E[E{aTb (ε, X)A(X)ε|X}].

(4.46)

But, by the deﬁnition of Λ1sb , E{ab (ε, X)εT |X} = 0q×d or, equivalently, E{abj (ε, X)εj |X} = 0, for all j = 1, . . . , q and j = 1, . . . , d, where {abj (ε, X) is the j-th element of ab (ε, X) and εj is the j -th element of ε. Consequently, the inner expectation of (4.46), which can be written as Ajj (X)E{abj (ε, X)εj |X}, j,j

where Ajj (X) is the (j, j )-th element of A(X), must also equal zero. This, in turn, proves (4.45). Now that we have shown the orthogonality of the spaces Λ1sb and the space (4.42), in order to prove that the space (4.42) is the orthogonal complement of Λ1sb , it suﬃces to show that any h ∈ H can be written as h1 + h2 , where h1 ∈ (4.42) and h2 ∈ Λ1sb . Or, equivalently, for any h ∈ H, there exists g q×d (X) such that {h(ε, X) − g(X)ε} ∈ Λ1sb . (4.47) That such a function g(X) exists follows by solving the equation E[{h − g(X)ε}εT |X] = 0q×d , or E(hεT |X) − g(X)E(εεT |X) = 0, which yields

g(X) = E(hεT |X){E(εεT |X)}−1 ,

where, to avoid any technical diﬃculties, we will assume that the conditional variance matrix E(εεT |X) is positive deﬁnite and hence invertible. We have thus demonstrated that, for the semiparametric restricted moment model, any element of the Hilbert space perpendicular to the nuisance tangent space is given by Aq×d (X) ε or A(X){Y − µ(X, β0 )}.

(4.48)

Inﬂuence functions of RAL estimators for β (i.e., ϕ(ε, X)) are normalized versions of elements perpendicular to the nuisance tangent space. That is, the space of inﬂuence functions, as well as being orthogonal to the nuisance tangent space, must also satisfy condition (i) of Theorem 4.2, namely that E{ϕ(ε, X)SβT (ε, X)} = I q×q , where Sβ (ε, X) is the score vector with respect to the parameter β and I q×q is the q × q identity matrix. If we start with any A(X), and deﬁne ϕ(ε, X) = C q×q A(X)ε, where C q×q is a q ×q constant matrix (i.e., normalization factor), then condition (i) of Theorem 4.2 is satisﬁed by solving

4.5 Semiparametric Restricted Moment Model

85

E{CA(X)εSβT (ε, X)} = I q×q or

C = [E{A(X)εSβT (ε, X)}]−1 .

(4.49)

Since a typical element orthogonal to the nuisance tangent space is given by A(X){Y − µ(X, β0 )}, and since a typical inﬂuence function is given by CA(X){Y − µ(X, β0 )}, where C is deﬁned by (4.49), this motivates us to consider an m-estimator for β of the form n

CA(Xi ){Yi − µ(Xi , β)} = 0.

i=1

Because C is a multiplicative constant matrix, then, as long as C is invertible, this is equivalent to solving the equation n

A(Xi ){Yi − µ(Xi , β)} = 0.

i=1

This logic suggests that estimators can often be motivated by identifying elements orthogonal to the nuisance tangent space, a theme that will be used frequently throughout the remainder of the book. We showed in Section 4.1 that solutions to such linear estimating equations result in semiparametric GEE estimators with inﬂuence function (4.4) that is proportional to A(X){Y − µ(X, β)}, where the proportionality constant is given by {E(AD)}−1 . In the next section, we will show that this proportionality constant satisﬁes equation (4.49). This will be a consequence of the results obtained in deriving the eﬃcient inﬂuence function, as we now demonstrate. The Eﬃcient Inﬂuence Function To derive an eﬃcient semiparametric estimator, we must ﬁnd the eﬃcient inﬂuence function. For this, we need to derive the eﬃcient score (i.e., the residual after projecting the score vector with respect to β onto the nuisance tangent space Λ) Seﬀ (ε, X) = Sβ (ε, X) − Π{Sβ (ε, X)|Λ}, which by (4.44) equals Seﬀ (ε, X) = E{Sβ (ε, X)εT |X}V −1 (X)ε, (4.50) where V (X) denotes E(εεT |X). Recall that the restricted moment model was characterized by {β, η1 (ε, x), η2 (x)}, where β is the parameter of interest and η1 (·), η2 (·) are inﬁnitedimensional nuisance parameters. When studying the nuisance tangent space, we ﬁxed β at the truth and varied η1 (·) and η2 (·). To compute Sβ , we ﬁx the nuisance parameters at the truth and vary β. A typical density in our model was given as η1 {y − µ(x, β), x}η2 (x),

86

4 Semiparametric Models

where η1 (ε, x) = pε|X (ε|x) and η2 (x) = pX (x). If we ﬁx the nuisance parameter at the truth (i.e., η10 (ε, x) and η20 (x)), then Sβ (y, x, β0 , η0 (·)) = =

∂ log η10 {y − µ(x, β), x} ∂β ∂η10 (ε, x)/∂β η10 (ε, x)

, β=β0

.

(4.51)

β=β0

Due to the model restriction, {y − µ(x, β)}η10 {y − µ(x, β), x}dy = 0

for all x, β,

we obtain ∂ ∂β T

{y − µ(x, β)}η10 {y − µ(x, β), x}dy

= 0. β=β0

Taking the derivative inside the integral, we obtain d×q ∂µ(x, β0 ) − η (ε, x)dε + εSβT {ε, x, β0 , η0 (·)}η10 (ε, x)dε = 0 10 ∂β T for all x, or

' ( −∂µ(X, β0 ) + E εSβT (ε, X)|X = 0. T ∂β

After solving the preceding equation and taking the transpose, we obtain DT (X) = E{Sβ (ε, X)εT |X},

(4.52)

where D(X) =

∂µ(X, β0 ) . ∂β T

By (4.50) and (4.52), we obtain that the eﬃcient score is Seﬀ (ε, X) = DT (X)V −1 (X)ε,

(4.53)

and the optimal estimator is obtained by solving the estimating equation

4.5 Semiparametric Restricted Moment Model n

DT (Xi )V −1 (Xi ){Yi − µ(Xi , β)} = 0

87

(4.54)

i=1

(optimal GEE). We also note that the normalization constant matrix C given in (4.49) involves the expectation E{A(X)εSβT (ε, X)}, which by a conditioning argument can be derived as E[A(X)E{εSβT (ε, X)|X}], which equals E{A(X)D(X)} by (4.52). Hence, C = [E{A(X)D(X)}]−1 . This implies that a typical inﬂuence function is given by [E{A(X)D(X)}]−1 A(X){Y − µ(X, β)}, which is the inﬂuence function for the GEE estimator given in (4.4). Similarly, the eﬃcient inﬂuence function can be obtained by using the appropriate normalization constant with the eﬃcient score (4.53) to yield [E{DT (X)V −1 (X)D(X)}]−1 DT (X)V −1 (X){Y − µ(X, β)}.

(4.55)

The semiparametric eﬃciency bound is given as T V = [E{Seﬀ (ε, X)Seﬀ (ε, X)}]−1 ,

which by (4.53) is equal to [E{DT (X)V −1 (X)εεT V −1 (X)D(X)}]−1 = [E{DT (X)V −1 (X)E(εεT |X)V −1 (X)D(X)}]−1 = [E{DT (X)V −1 (X)D(X)}]−1 .

(4.56)

This, of course, is also the variance of the eﬃcient inﬂuence function (4.54). A Diﬀerent Representation for the Restricted Moment Model Up to now, we have deﬁned probability models for the restricted moment model when the response variable Y was continuous. This allowed us to consider conditional densities for ε given X, where ε = Y −µ(X, β). We were able to do this because we assumed that Y was a continuous variable with respect to Lebesgue measure and hence the transformed variable ε was also a continuous variable with respect to Lebesgue measure. This turned out to be useful, as we could then describe the semiparametric model through the ﬁnite-dimensional parameter of interest β together with the inﬁnite-dimensional nuisance parameters η1 (ε, x) and η2 (x), where β was variationally independent of η1 (ε, x) and η2 (x). That is, any combination of β, η1 (ε, x), and η2 (x) would lead to a valid density describing our semiparametric model. There are many problems, however, where we want to use the restricted moment model with a dependent variable Y that is not a continuous random

88

4 Semiparametric Models

variable. Strictly speaking, the response variable CD4 count used in the loglinear model example of Section 4.1 is not a continuous variable. A more obvious example is when we have a binary response variable Y taking on values 1 (response) or 0 (nonresponse). A popular model for modeling the probability of response as a function of covariates X is the logistic regression model. In such a model, we assume P (Y = 1|X) =

exp(β T X ∗ ) , 1 + exp(β T X ∗ )

where X ∗ = (1, X T )T , allowing the introduction of an intercept term. Since Y is a binary indicator, this implies that E(Y |X) = P (Y = 1|X), and hence the logistic regression model is just another example of a restricted moment exp(β T X ∗ ) model with µ(X, β) = 1+exp(β T X∗) . The diﬃculty that occurs when the response variable Y is not a continuous random variable is that the transformed variable Y − µ(X, β) may no longer have a dominating measure that allows us to deﬁne densities. In order to address this problem, we will work directly with densities deﬁned on (Y, X), namely p(y, x) with respect to some dominating measure νY × νX . As you will see, many of the arguments developed previously will carry over to this more general setting. We start by ﬁrst deriving the nuisance tangent space. As before, we need to ﬁnd parametric submodels. Let p(y, x) be written as p(y|x) p(x), where p(y|x) is the conditional density of Y given X and p(x) is the marginal density of X, and denote the truth as p0 (y, x) = p0 (y|x)p0 (x). The parametric submodel can be written generically as p(y|x, β, γ1 )p(x, γ2 ), where for some β0 , γ10 , γ20 p0 (y|x) = p(y|x, β0 , γ10 ) and p0 (x) = p(x, γ20 ). The parametric submodel nuisance tangent space is the space spanned by the score vector with respect to the nuisance parameters γ1 and γ2 . As in the previous section, the parametric submodel nuisance tangent space can be written as the direct sum of two orthogonal spaces Λ γ1 ⊕ Λ γ2 , Λ γ1 ⊥ Λ γ2 , where ( ' Λγ1 = B q×r1 Sγ1 (Y, X) for all B q×r1 , ( ' Λγ2 = B q×r2 Sγ2 (X) for all B q×r2 , Sγ1 (y, x) =

∂ log p(y|x, β0 , γ10 ) , ∂γ1

4.5 Semiparametric Restricted Moment Model

and Sγ2 (x) =

89

∂ log p(x, γ20 ) . ∂γ2

Hence, the semiparametric nuisance tangent space Λ equals Λ1s ⊕ Λ2s , Λ1s ⊥ Λ2s , where Λ1s = {mean-square closure of all Λγ1 } and Λ2s = {mean-square closure of all Λγ2 }. We showed previously that ⎫ ⎧ all q-dimensional mean-zero ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ measurable functions of X . Λ2s = with ﬁnite second moments; i.e., ⎪ ⎪ ⎪ ⎪ ⎭ ⎩ q×1 α (X) : E{α(X)} = 0 We now consider the space Λ1s . Again, we proceed by making educated guesses for the nuisance tangent space by considering the structure of the parametric submodel nuisance tangent space and then verify that our guess is correct. For the restricted moment model, if we ﬁx β at the truth, (β0 ), then the conditional densities p(y|x, β0 , γ1 ) must satisfy p(y|x, β0 , γ1 )dν(y) = 1 for all x, γ1 (4.57) and yp(y|x, β0 , γ1 )dν(y) = µ(x, β0 )

for all x, γ1 .

(4.58)

Using standard arguments, where we take derivatives of (4.57) and (4.58) with respect to γ1 , interchange integration and diﬀerentiation, divide and multiply by p(y|x, β0 , γ1 ), and set γ1 at the truth, we obtain Sγ1 (y, x)p0 (y|x)dν(y) = 0r1 ×1 for all x and ySγT1 (y, x)p0 (y|x)dν(y) = 0d×r1

for all x.

That is, E{Sγ1 (Y, X)|X} = 0r1 ×1 and E{Y SγT1 (Y, X)|X} = 0d×r1 . This implies that any element of Λγ1 , namely B q×r1 Sγ1 (Y, X), would satisfy

90

4 Semiparametric Models

E{B q×r1 Sγ1 (Y, X)|X} = 0q×1 and E{B q×r1 Sγ1 (Y, X)Y T |X} = 0q×d . This leads us to the conjecture that (conj) Λ1s = aq×1 (Y, X) : E{a(Y, X)|X} = 0q×1 and E{a(Y, X)Y T |X} = 0q×d . To verify this conjecture, we consider an arbitrary bounded element (conj) aq×1 (Y, X) ∈ Λ1s . We construct the parametric submodel p0 (y|x){1 + T γ1 a(y, x)} with γ1 chosen suﬃciently small to ensure {1 + γ1T a(y, x)} ≥ 0 for all y, x. This parametric submodel contains the truth when γ1 = 0 and satisﬁes the constraints of the restricted moment model, namely p(y|x, γ1 )dν(y) = 1 for all x, γ1 , yp(y|x, γ1 )dν(y) = µ(x, β0 ) for all x, γ1 . The score vector Sγ1 (Y, X) for this parametric submodel is a(Y, X). Also any (conj) (conj) element of Λ1s can be derived as limits of bounded elements in Λ1s . Therefore, we have established that any element of a parametric submodel (conj) (conj) , and any element of Λ1s nuisance tangent space Λγ1 is an element of Λ1s is either an element of a parametric submodel nuisance tangent space or a limit (conj) . of such elements. Thus, we conclude that Λ1s = Λ1s Note that Λ1s can be expressed as Λ1sa ∩ Λ1sb , where Λ1sa = {aq×1 (Y, X) : E{a(Y, X)|X} = 0q×1 } and Λ1sb = {aq×1 (Y, X) : E{a(Y, X){Y − µ(X, β0 )}|X} = 0q×d }. Therefore the nuisance tangent space Λ = Λ2s ⊕ (Λ1sa ∩ Λ1sb ). This representation is useful because Λ2s ⊕ Λ1sa = H (the whole Hilbert space) and Λ2s ⊂ Λ1sb . Consequently, we use Lemmas 4.3–4.5 to show that the semiparametric nuisance tangent space Λ = Λ1sb . Using the exact same proof as for Theorem 4.8, we can show that h(Y, X) − Π[h|Λ1sb ]

or

Π[h|Λ⊥ 1sb ]

= E{h(Y, X)(Y − µ(X, β0 ))T |X}V −1 (X){Y − µ(X, β0 )}. To complete the development of the semiparametric theory, we still need to derive the eﬃcient score or

4.5 Semiparametric Restricted Moment Model

91

Seﬀ (Y, X) = Π[Sβ (Y, X)|Λ⊥ ] = E[Sβ (Y, X){(Y − µ(X, β0 )}T |X]V −1 (X){Y − µ(X, β0 )}.

(4.59)

Since E(Y |X = x) = µ(x, β), then for any parametric submodel where p(y|x, β, γ1 ) satisﬁes yp(y|x, β, γ1 )dν(y) =µ(x, β) for all x, γ1

(4.60)

and p(y|x, β0 , γ10 ) =p0 (y|x), assuming at least one such parametric submodel exists, we can diﬀerentiate both sides of (4.60) with respect to β T , interchange integration and diﬀerentiation, divide and multiply by p0 (y|x), and set β and γ1 equal to β0 and γ10 , respectively, to obtain E{Y SβT (Y, X)|X} = E[{Y − µ(X, β0 )}SβT (Y, X)|X] = D(X), where D(X) =

(4.61) ∂µ(X, β0 ) . ∂β T

Equation (4.61) follows because E{µ(X, β0 )SβT (Y, X)|X} = µ(X, β0 )E{SβT (Y, X)|X} = 0. Taking transposes yields E[Sβ (Y, X){Y − µ(X, β0 )}T |X] = DT (X). Consequently, the eﬃcient score (4.59) is given by Seﬀ (Y, X) = DT (X)V −1 (X){Y − µ(X, β0 )}. It still remains to show that a parametric submodel exists that satisﬁes (4.60). This is addressed by the following argument. Existence of a Parametric Submodel for the Arbitrary Restricted Moment Model A class of joint densities for (Y, X) can be deﬁned with dominating measure νY ×νX that satisfy E(Y |X) = µ(X, β) by considering the conditional density

92

4 Semiparametric Models

of Y given X multiplied by the marginal density of X, where a class of conditional densities for Y given X can be constructed using exponential tilting. To illustrate, let us consider, for simplicity, the case where Y is a univariate bounded random variable. We will assume that, at the truth, the conditional density of Y given X is given by p0 (y|x), where yp0 (y|x)dν(y) = µ(x, β0 ) for all x. The question is whether we candeﬁne a class of conditional densities p(y|x, β) with respect to νY such that yp(y|x, β)dν(y) = µ(x, β) for all x and for β in a neighborhood of β0 . We consider the conditional densities p0 (y|x) exp{c(x, β)y} , exp{c(x, β)y}p0 (y|x)dν(y) where c(x, β), if possible, is chosen so that yp(y|x, β)dν(y) = µ(x, β) for β in a neighborhood of β0 . We ﬁrst note that we can take c(x, β0 ) to be equal to zero because, by deﬁnition, yp0 (y|x)dν(y) = µ(x, β0 ). To illustrate that c(x, β) exists and can be uniquely deﬁned in a neighborhood of β0 , we ﬁx the value of x and consider the function p0 (y|x) exp(cy) dν(y) y exp(cy)p0 (y|x)dν(y) p(y|x, β) =

as a function in c, which can also be written as E0 {Y exp(cY )|X = x} , E0 {exp(cY )|X = x}

(4.62)

where the conditional expectation E0 (·|X = x) is taken with respect to the conditional density p0 (y|x). Taking the derivative of (4.62) with respect to c, we obtain 2 E0 {Y exp(cY )|X = x} E0 {Y 2 exp(cY )|X = x} − . E0 {exp(cY )|X = x} E0 {exp(cY )|X = x} This derivative, being the conditional variance of Y given X = x with respect p0 (y|x) exp(cy) to the conditional density exp(cy)p , must therefore be positive, im0 (y|x)dν(y) plying that the function (4.62) is strictly monotonically increasing in c. Hence, in a neighborhood of β about the value β0 , a unique inverse for c exists in a neighborhood of zero that satisﬁes the equation E0 {Y exp(cY )|X = x} = µ(x, β). E0 {exp(cY )|X = x} We deﬁne this solution as c(x, β). The arguments above can be generalized to multivariate Y by using the inverse function theorem.

4.6 Adaptive Semiparametric Estimators for the Restricted Moment Model

93

4.6 Adaptive Semiparametric Estimators for the Restricted Moment Model Using the theory we have developed for the semiparametric restricted moment model, we now know that the class of all inﬂuence functions for semiparametric RAL estimators for β must be of the form [E{A(X)D(X)}]−1 A(X){Y − µ(X, β0 )} for any arbitrary q × d matrix, A(X), of functions of X. We also showed that the solution to the estimating equation n

A(Xi ){Yi − µ(Xi , β)} = 0

(4.63)

i=1

results in an estimator for β that is RAL with inﬂuence function given by (4.4). The estimating equation (4.63) is an example of what Liang and Zeger (1986) referred to as a linear estimating equation or a GEE estimator. Using semiparametric theory, we showed that this class of estimators encompasses, at least asymptotically, all possible semiparametric RAL estimators for β in a restricted moment model. That is, any semiparametric RAL estimator must have an inﬂuence function that is contained within the class of inﬂuence functions for GEE estimators. Consequently, it is reasonable to restrict attention to only such estimators and, moreover, the eﬃcient RAL estimator must be asymptotically equivalent to the eﬃcient GEE estimator given by the solution to (4.54). It is important to note that the optimal estimating equation depends on using the correct V (X), where V (X) = E(εεT |X) is the conditional variance of ε given X, or equivalently, the conditional variance of Y given X. Since, in a semiparametric model, the distribution of ε given X is left unspeciﬁed, the function V (x) is unknown to us. We can try to estimate this conditional variance of Y as a function of X = x using the data, but generally, without additional assumptions, this requires smoothing estimators that are not very stable, especially if X is multidimensional. Consequently, substituting such nonparametric type smoothing estimators Vˆ (X) into equation (4.54) leads to estimators for β that perform poorly with moderate sample sizes. Another strategy is to posit some relationship for V (x), either completely speciﬁed or as a function of a ﬁnite (small) number of additional parameters ξ as well as the parameters β in the restricted moment model. This is referred to as a working variance assumption because the model V (x, ξ, β) for the conditional variance of Y given X = x may not contain the truth. For example,

94

4 Semiparametric Models

suppose, for simplicity, we take the response variable Y to be one-dimensional (i.e., d = 1) and assume the variance function V (x, ξ) = exp(ξ0 + ξ1T x), where ξ1 is a vector of dimension equal to the number of covariates that make up the vector X. For this illustration, we chose a log-linear relationship to ensure a positive variance function. We might choose such a model for the variance not necessarily because we believe this is the true relationship but rather because if we believe that the variance is related to the covariates, then this model may capture some of this relationship, at least to a ﬁrst order. Another model, if one believes that the conditional variance of Y given X may be related to the conditional mean of Y given X, is to assume that V (x, ξ, β) = ξ02 {µ(x, β)}ξ1 ,

(4.64)

where ξ0 and ξ1 are scalar constants. Again, we may not believe that this captures the true functional relationship of the conditional variance to the conditional mean but may serve as a useful approximation. Nonetheless, if, for the time being, we accepted V (x, ξ, β) as a working model, then the parameters ξ in V (x, ξ, β) can be estimated separately using the squared residuals {Yi − µ(Xi , βˆninitial )}2 , i = 1, . . . , n, where βˆninitial is some initial consistent estimator for β. For instance, we can ﬁnd an initial estimator for β by solving equation (4.1) using A(X, β) = D(X, β) (which is equivalent to a working variance V (X) proportional to the identity matrix). Using this initial estimator, we can then ﬁnd an estimator for ξ by solving the equation n

Q(Xi , ξ, βˆninitial ) {Yi − µ(Xi , βˆninitial )}2 − V (Xi , ξ, βˆninitial ) = 0,

i=1

where Q(X, ξ, β) is an arbitrary vector of functions of X, ξ, and β of dimension equal to the dimension of ξ. One possibility is to choose Q(X, ξ, β) = ∂V (X, ξ, β)/∂ξ. Denote the resulting estimator by ξˆn . Under weak regularity conditions, ξˆn will converge in probability to some constant ξ ∗ whether the variance function was correctly speciﬁed or not. Without going into the technicalities, it can be shown that substituting V (Xi , ξˆn , βˆninitial ) into equation (4.54) will result in an RAL estimator for β, namely βˆn (not to be confused with βˆninitial ), with inﬂuence function [E{A(X)D(X)}]−1 A(X){Y − µ(X, β)}, where A(X) = DT (X)V −1 (X, ξ ∗ , β0 ). Consequently, solutions to such estimating equations, where we use a working variance assumption, will lead to what is called a locally eﬃcient estimator for β. That is, if the posited relationship for V (x, ξ, β) is indeed true (i.e., if the true conditional variance of Y given X = x is contained in the model V (x, ξ, β), with V (x, ξ0 , β0 ) denoting the truth), then V (x, ξˆn , βˆninitial ) will converge to V (x, ξ0 , β0 ) = var(Y |X = x) and the resulting estimator is semiparametric eﬃcient; otherwise, it is not. However, even if the posited model is not correct (i.e.,

4.6 Adaptive Semiparametric Estimators for the Restricted Moment Model

95

V (x, ξ ∗ , β0 ) = var(Y |X = x)), V (X, ξ ∗ , β0 ) is still a function of X and the resulting estimator for β is consistent and asymptotically normal. Such adaptive estimators for β have been shown empirically to have high relative eﬃciency compared with the optimal semiparametric eﬃciency bound if the working variance model provides a good approximation to the truth. When using a working variance, one must be careful in estimating the asymptotic variance of the estimator βˆn . Since the asymptotic variance of the eﬃcient inﬂuence function is given by (4.56), there is a natural temptation to estimate the asymptotic variance by using an estimator for [E{DT (X)V −1 (X, ξ ∗ , β0 )D(X)}]−1 , namely n

−1

n

"−1 −1 initial ˆ ˆ ˆ ˆ D (Xi , βn )V (Xi , ξn , βn )D(Xi , βn ) . T

i=1

This estimator would only be a consistent estimator for the asymptotic variance of βˆn if the working variance contained the truth. Otherwise, it would be asymptotically biased. A consistent estimator for the asymptotic variance can be obtained by using the sandwich variance given by (4.14) with A(X) = DT (X, βˆn )V −1 (X, ξˆn , βˆninitial ). Example 1. Logistic regression model We argued earlier that the logistic regression model is an example of a restricted moment model where the response variable Y is a binary variable exp(β T X ∗ ) ∗ taking on the values 1 or 0 and µ(X, β) = 1+exp(β = (1, X T )T . T X ∗ ) , where X Accordingly, from the theory just developed, we know that the inﬂuence functions of all RAL estimators for β can be derived as the solution to the generalized estimating equations n

A(Xi ){Yi − µ(Xi , β)} = 0

i=1

for arbitrary Aq×1 (X), and the eﬃcient estimator is obtained by choosing 0) and V (X) = var(Y |X). A(X) = DT (X)V −1 (X), where D(X) = ∂µ(X,β ∂β T Because Y is binary, V (X) = var(Y |X) = µ(X, β0 ){1 − µ(X, β0 )} =

exp(β0T X ∗ ) . {1 + exp(β0T X ∗ )}2

Taking derivatives, we also obtain that DT (X) = X ∗ V (X). Hence the optimal estimator for β can be derived by choosing A(X) = DT (X)V −1 (X) = X ∗ , leading us to the optimal estimating equation n i=1

Xi∗ Yi −

exp(β T Xi∗ ) 1 + exp(β T Xi∗ )

= 0.

(4.65)

96

4 Semiparametric Models

Because the conditional distribution of Y given X is fully described with the ﬁnite number of parameters β, we could also estimate the parameter β using maximum likelihood. It is an easy exercise to show that the solution to (4.65) also leads to the maximum likelihood estimator for β. Example 2. Log-linear model In Section 4.1, we considered a log-linear model to model CD4 count as a function of covariates; see (4.10). We also proposed an ad hoc semiparametric GEE estimator for β as the solution to (4.11) without any motivation. Let us study this problem more carefully. We know that the optimal semiparametric estimator for β for model (4.10) is given as the solution to the equation n

DT (Xi , β)V −1 (Xi ){Yi − µ(Xi , β)} = 0,

(4.66)

i=1

where the gradient matrix D(X, β) was derived in Section 4.1 for the log-linear model to be D(X, β) = ∂µ(X,β) = µ(X, β)(1, X T ). Although the semipara∂β T metric restricted moment model makes no assumptions about the variance function V (X) = var(Y |X), we argued that to ﬁnd a locally eﬃcient estimator for β we might want to make some assumptions regarding the function V (X) and derive an adaptive estimator. Since CD4 count is a count we might be willing to assume that it follows a Poisson distribution. If indeed the distribution of Y given X follows a Poisson distribution with mean µ(X, β), then we immediately know that V (X) = µ(X, β). Although this is probably too strong an assumption to make in general, we may believe that a good approximation is that the variance function V (X) is at least proportional to the mean; i.e., that V (X) = σ 2 µ(X, β), where σ 2 is some unknown scale factor. In that case, DT (X, β)V −1 (X) = σ −2 (1, X T )T and the locally eﬃcient estimator for β would be the solution to (4.66), which, up to a proportionality constant, would be the solution to the estimating equation n

(1, XiT )T {Yi − µ(Xi , β)} = 0,

(4.67)

i=1

which is the same as the estimator proposed in Section 4.1; see (4.11). Thus, we have shown that the locally eﬃcient semiparametric estimator for β, when the conditional distribution of the response variable given the covariates follows a Poisson distribution or, more generally, if the conditional variance of the response variable given the covariates is proportional to the conditional mean of Y given the covariates, is given by (4.67). For a more detailed discussion on log-linear models, see McCullagh and Nelder (1989, Chapter 6). If the conditional variance of Y given X is not proportional to the conditional mean µ(X, β), then the estimator (4.67) is no longer semiparametric

4.6 Adaptive Semiparametric Estimators for the Restricted Moment Model

97

eﬃcient; nonetheless, it will still be a consistent, asymptotically normal estimator for β with an asymptotic variance that can be estimated using the sandwich estimator (4.14). Another possibility, which would give somewhat greater ﬂexibility, is to model the variance function using (4.64), as this model contains the Poisson variance structure mentioned above, and use the adaptive methods described in this section to estimate β.

Extensions of the Restricted Moment Model When considering the restricted moment model, we have concentrated on models where E(Y |X) = µ(X, β) or, equivalently, E{ε(Y, X, β)|X} = 0, where ε(Y, X, β) = Y − µ(X, β). Using this second representation, the theory developed for the restricted moment problem can be applied (or extended) to models where E{ε(Y, X, β)|X} = 0 for arbitrary functions ε(Y, X, β). This allows us to consider models, for example, where we model both the conditional variance and the conditional mean of Y given X. Say we want a model where we assume that E(Y |X) = µ(X, β) and var(Y |X) = V (X, β, ξ) and our interest is in estimating the parameters β and ξ using a sample of data that are realizations of (Yi , Xi ), i = 1, . . . , n. For simplicity, take Y to be a univariate response random variable, although this can be easily generalized to multivariate response random vectors as well. We could then deﬁne the bivariate vector ε(Y, X, β, ξ) = {ε1 (Y, X, β, ξ), ε2 (Y, X, β, ξ)}T , where ε1 (Y, X, β, ξ) = Y − µ(X, β) and

ε2 (Y, X, β, ξ) = {Y − µ(X, β)}2 − V (X, β, ξ).

With such a representation, it is clear that our model for the conditional mean and conditional variance of Y given X is equivalent to E{ε(Y, X, β, ξ)|X} = 0. This representation also allows us to consider models for the conditional quantiles of Y as a function of X. For example, suppose we wanted to consider a model for the median of a continuous random variable Y as a function of X. Say we wanted a model where we assumed that the conditional median of Y given X was equal to µ(X, β) and we wanted to estimate β using a sample of data (Yi , Xi ), i = 1, . . . , n. This could be accomplished by considering ε(Y, X, β) = I{Y ≤ µ(X, β)} − .5 because the conditional expectation of ε(Y, X, β) given X is given by E{ε(Y, X, β)|X} = P {Y ≤ µ(X, β)|X} − .5 and, by deﬁnition, the conditional median is the value µ(X, β) such that the conditional probability that Y is less than or equal to µ(X, β), given X, is equal to .5, which would imply that E{ε(Y, X, β)|X} = 0.

98

4 Semiparametric Models

Using arguments similar to those developed in this chapter, we can show that we can restrict attention to semiparametric estimators that are solutions to the estimating equations n

A(Xi )ε(Yi , Xi , β) = 0

i=1

for arbitrary A(X) and that the eﬃcient semiparametric estimator for β falls within this class. For models where we include both the conditional mean and conditional variance, this leads to the so-called quadratic estimating equations or GEE2. We give several exercises along these lines.

4.7 Exercises for Chapter 4 1. Prove that the linear subspacesTj , j = 1, . . . , m are mutually orthogonal subspaces, where Tj is deﬁned by (4.21) of Theorem 4.5. That is, show that hj is orthogonal to hj , where hj ∈ Tj , hj ∈ Tj and j = j , j, j = 1, . . . , m. 2. Let Y be a one-dimensional response random variable. Consider the model Y = µ(X, β) + ε, where β ∈ Rq , and E{h(ε)|X} = 0 for some arbitrary function h(·). Up to now, we considered the identity function h(ε) = ε, but this can be generalized to arbitrary h(ε). For example, if we deﬁne h(ε) = {I(ε ≤ 0) − 1/2}, then this is the median regression model. That is, if we deﬁne F (y|x) = P (Y ≤ y|X = x), then med (Y |x) = F −1 (1/2, x), the value m(x) such that F (m(x)|x) = 1/2. Therefore, the model with this choice of h(·) is equivalent to med (Y |X) = µ(X, β). Assume no other restrictions are placed on the model but E{h(ε)|X} = 0 for some function h(·). For simplicity, assume h is diﬀerentiable, but this can be generalized to nondiﬀerentiable h such as in median regression. a) Find the space Λ⊥ (i.e., the space perpendicular to the nuisance tangent space). b) Find the eﬃcient score vector for this problem. c) Describe how you would construct a locally eﬃcient estimator for β from a sample of data (Yi , Xi ), i = 1, . . . , n. d) Find an estimator for the asymptotic variance of the estimator deﬁned in part (c). 3. Letting Y be a one-dimensional response variable, consider the semiparametric model where

4.7 Exercises for Chapter 4

99

E(Y |X) = µ(X, β) and Var (Y |X) = V (X, β),

β ∈ Rq ,

where V (x, β) > 0 for all x and β. a) Derive the space Λ⊥ (i.e., the space orthogonal to the nuisance tangent space). b) Find the eﬃcient score vector. c) Derive a locally eﬃcient estimator for β using a sample of data (Yi , Xi ), i = 1, . . . , n.

5 Other Examples of Semiparametric Models

In this chapter, we will consider other widely used semiparametric models. We begin with the “location-shift” regression model and the proportional hazards regression model. These models, like the restricted moment regression model discussed in detail in Chapter 4, are best represented through a parameter of interest β and an inﬁnite-dimensional nuisance parameter η. This being the case, the class of inﬂuence functions of RAL estimators for β will be deﬁned as elements of a Hilbert space orthogonal to the nuisance tangent space. Later in the chapter, we will also discuss the problem of estimating the mean treatment diﬀerence between two treatments in a randomized pretest-posttest study or, more generally, in a randomized study with covariate adjustment. We will show that this seemingly easy problem can be cast as a semiparametric problem for which the goal is to ﬁnd an eﬃcient estimator for the mean treatment diﬀerence. We will see that this problem is best represented by deﬁning the model through an inﬁnite-dimensional parameter θ, where the parameter of interest, the mean treatment diﬀerence, is given by β(θ), a function of θ. With this representation, it will be more convenient to deﬁne the tangent space and its orthogonal complement and then ﬁnd the eﬃcient estimator by considering the residual after projecting the inﬂuence function of a simple, but ineﬃcient, RAL estimator onto the orthogonal complement of the tangent space. This methodology will be described in detail in Section 5.4 that follows.

5.1 Location-Shift Regression Model Let Yi denote a continuous response variable and Xi a vector of covariates measured on the i-th individual of an iid sample i = 1, . . . , n. For simplicity, we will only consider a univariate response variable here, but all the arguments that follow could be generalized to multivariate response variables as well. A popular way of modeling the relationship of Yi as a function of Xi is with a location-shift regression model. That is, consider the model where

102

5 Other Examples of Semiparametric Models

Yi = µ(Xi , β) + εi , i = 1, . . . , n, (Yi , Xi ), i = 1, . . . , n are iid, Yi is assumed to be continuous, and εi (also continuous) is independent of Xi (denoted as εi ⊥ ⊥ Xi ). These models may be linear or nonlinear models depending on whether µ(Xi , β) is linear in β or not. The semiparametric properties of this model were studied by Bickel et al. (1993, Section 4.3) and Manski (1984). In this model, there is an assumed basic underlying distributional shape that the density of the response variable Yi follows, that of the distribution of εi , but where the location of this distribution is determined according to the value of the covariates Xi (i.e., shifted by µ(Xi , β)). The q-dimensional parameter β determines the magnitude of the shift in the location of the distribution as a function of the covariates, and it is this parameter that is of primary interest. We make no additional assumptions on the distribution of εi or Xi . To avoid identiﬁability problems, we will also assume that if (α1 , β1 ) = (α2 , β2 ), then α1 + µ(X, β1 ) = α2 + µ(X, β2 ), where α1 , α2 are any scalar constants and β1 , β2 are values in the parameter space contained in Rq . For example, if we consider a linear model where µ(Xi , β) = XiT β, we must make sure not to include an intercept term, as this will be absorbed into the error term εi ; i.e., Yi = β1 Xi1 + · · · + βq Xiq + εi , i = 1, . . . , n.

(5.1)

The location-shift regression model is semiparametric because no restrictions are placed on the distribution of εi or Xi . Such a model can be characterized by {β, pε (ε), pX (x)}, where pε (ε) and pX (x) are arbitrary densities of ε and X, respectively. We assume that ε and hence Y is a continuous random variable with dominating Lebesgue measure ε or Y , respectively. The dominating measure for X is denoted by νX . An arbitrary density in this model for a single observation is given by pY,X (y, x) = pε {y − µ(x, β)}pX (x) with respect to the dominating measure Y × νX . It has been my experience that there is confusion between the locationshift regression model and the restricted moment model. In many introductory courses in statistics, a linear regression model is deﬁned as Yi = α + XiT β + εi , i = 1, . . . , n,

(5.2)

where εi , i = 1, . . . , n are assumed to be iid mean-zero random variables. Sometimes, in addition, it may be assumed that εi are normally distributed

5.1 Location-Shift Regression Model

103

with mean zero and some common variance σ 2 . What is often not made clear is that there is an implicit assumption that the covariates Xi , i = 1, . . . , n are ﬁxed. Consequently, the error terms εi being iid implies that the distribution of εi is independent of Xi . Thus, such models are examples of what we are now calling the location-shift regression models. In contrast, a linear restricted moment model can also be written as (5.2), where εi , i = 1, . . . , n are iid random variables. However, the restricted moment model makes the assumption that E(εi |Xi ) = 0, which then implies that E(εi ) = 0 but does not necessarily assume that εi is independent of Xi . The location-shift regression model, although semiparametric, is more restrictive than the restricted moment model considered in Chapter 4. For example, if we consider the linear restricted moment model Yi = α + β1 Xi1 + · · · + βq Xiq + εi , where E (εi |Xi ) = 0, or equivalently Yi = β1 Xi1 + · · · + βq Xiq + (α + εi ), where E{α + εi |Xi } = α, then this model includes a larger class of probability distributions than the linear location-shift regression model; namely, Yi = β1 Xi1 + . . . + βq Xiq + ε∗i , where ε∗i is independent of Xi . Because of independence, we obtain E(ε∗i |Xi ) = E(ε∗i ) = constant, which satisﬁes the linear moment restriction, but, conversely, E(α + εi |Xi ) = α, which holds true for the restricted moment model, does not imply that (α + εi ) = ε∗i ⊥ ⊥ Xi . Since the location-shift regression model is more restrictive than the restricted moment model, we would expect the class of semiparametric RAL estimators for β for the location-shift regression model to be larger than the class of semiparametric RAL estimators for β for the restricted moment model and the semiparametric eﬃciency bound for the location-shift regression model to be smaller than the semiparametric eﬃciency bound for the restricted moment model. The Nuisance Tangent Space and Its Orthogonal Complement for the Location-Shift Regression Model The key to ﬁnding semiparametric RAL estimators for β and identifying the eﬃcient such estimator is to derive the space of inﬂuence functions of RAL estimators for β. This will require us to ﬁnd the space orthogonal to the

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5 Other Examples of Semiparametric Models

nuisance tangent space. The nuisance tangent space is deﬁned as the meansquare closure of all parametric submodel nuisance tangent spaces. The nuisance tangent space and its orthogonal complement for the semiparametric location-shift regression model are given as follows. Theorem 5.1. Using the convention that ε(β) = Y −µ(X, β) and ε = ε(β0 ) = Y −µ(X, β0 ), the nuisance tangent space for the location-shift regression model is given by Λ = Λ1s ⊕ Λ2s , where

q×1 q×1 , (ε) : E{a (ε)} = 0 Λ1s = aq×1 1 1 Λ2s = aq×1 (X) : E{aq×1 (X)} = 0q×1 , 2 2

and Λ1s ⊥ Λ2s . Proof. Consider the parametric submodel with density pε {y − µ(x, β), γ1 }pX (x, γ2 ),

(5.3)

where γ10 and γ20 denote the “truth.” If we ﬁx β at the truth β0 , then pε (ε, γ1 ) allows for an arbitrary marginal density of ε. Consequently, using logic developed in Chapter 4, the mean-square closure for parametric submodel nuisance tangent spaces Λγ1 = {B q×r1 Sγ1 (ε) for all B q×r1 }, where Sγ1 (ε) = ∂ log pε (ε, γ10 )/∂γ1 , is the space Λ1s , deﬁned as Λ1s = aq×1 (ε) : E{aq×1 (ε)} = 0q×1 . 1 1 Similarly, the mean-square closure for parametric submodel nuisance tangent spaces Λγ2 = {B q×r2 Sγ2 (X) for all B q×r2 }, where Sγ2 (x) = ∂ log pX (x, γ20 )/∂γ2 , is the space Λ2s , deﬁned as Λ2s = aq×1 (X) : E{aq×1 (X)} = 0q×1 . 2 2 Since the density (5.3) is a product involving variationally independent parameters γ1 and γ2 , the nuisance tangent space (i.e., the mean-square closure of all parametric submodel nuisance tangent spaces associated with arbitrary nuisance parameters γ1 and γ2 ) is given by Λ = Λ1s ⊕ Λ2s . Because ε is independent of X, it is easy to verify that Λ1s ⊥ Λ2s .

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105

Inﬂuence functions of RAL estimators for β lie in the space orthogonal to the nuisance tangent space. We can ﬁnd all elements of Λ⊥ by considering Λ⊥ = [{h − Π (h|Λ)} for all h ∈ H] . Because Λ1s ⊥ Λ2s , we obtain the following intuitive result. Theorem 5.2. Let Λ = Λ1s ⊕ Λ2s , where the closed linear subspaces Λ1s and Λ2s are orthogonal; i.e., Λ1s ⊥ Λ2s . Then Π(h|Λ) = Π(h|Λ1s ) + Π(h|Λ2s ).

(5.4)

Proof. The projection of any element h ∈ H onto the closed linear space Λ is the unique element Π(h|Λ) such that the residual h − Π(h|Λ) is orthogonal to every element in Λ = {(a1 + a2 ), where a1 ∈ Λ1s and a2 ∈ Λ2s }. To verify that (5.4) is true, we ﬁrst note that Π(h|Λ1s ) ∈ Λ1s and Π(h|Λ2s ) ∈ Λ2s , implying that Π(h|Λ1s ) + Π(h|Λ2s ) ∈ Λ. To complete the proof, we must show that the inner product

h − Π(h|Λ1s ) − Π(h|Λ2s ), a1 + a2 = 0

(5.5)

for all a1 ∈ Λ1s and a2 ∈ Λ2s . The inner product (5.5) can be written as

h − Π(h|Λ1s ), a1 − Π(h|Λ2s ), a1 + h − Π(h|Λ2s ), a2 − Π(h|Λ1s ), a2 .

(5.6) (5.7) (5.8) (5.9)

Since h − Π(h|Λ1s ) is orthogonal to Λ1s and h − Π(h|Λ2s ) is orthogonal to Λ2s , this implies that the inner products (5.6) and (5.8) are equal to zero. Also, since Π(h|Λ2s ) ∈ Λ2s and Π(h|Λ1s ) ∈ Λ1s , then Λ1s ⊥ Λ2s implies that the inner products (5.7) and (5.9) are also equal to zero. In the proof of Lemma 4.3, we showed that for any h(ε, X) ∈ H the projection of h(ε, X) onto Λ2s is given by Π(h|Λ2s ) = E{h(ε, X)|X}. Similarly, we can show that Π(h|Λ1s ) = E{h(ε, X)|ε}. Consequently, the space orthogonal to the nuisance tangent space is given by ⊥ Λ = [h(ε, X) − E{h(ε, X)|ε} − E{h(ε, X)|X}] for all h ∈ H . (5.10)

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5 Other Examples of Semiparametric Models

Semiparametric Estimators for β Since inﬂuence functions of RAL estimators for β are, up to a proportionality constant, deﬁned by the elements orthogonal to the nuisance tangent space, we now use the result in (5.10) that deﬁnes elements orthogonal to the nuisance tangent space to aid us in ﬁnding semiparametric estimators of β in the location-shift regression model. We begin by considering any arbitrary q-dimensional function of ε, X, say g q×1 (ε, X). In order that this be an arbitrary element of H, it must have mean zero. Therefore, we center g(ε, X) and deﬁne h(ε, X) = g(ε, X) − E{g(ε, X)}. We now consider an arbitrary element of Λ⊥ , which by (5.10) is given by {h − Π (h|Λ)}, which equals g(ε, X) − Gε (X) − GX (ε) + E{g(ε, X)},

(5.11)

where Gε (X) = E{g(ε, X)|X}, GX (ε) = E{g(ε, X)|ε}, and the function g(ε, X) should not equal g1 (ε) + g2 (X) in order to ensure that the residual (5.11) is not trivially equal to zero. Because of the independence of ε and X, we obtain, for ﬁxed X = x, that Gε (x) = E{g(ε, x)} and, for ﬁxed ε = ε∗ , that GX (ε∗ ) = E{g(ε∗ , X)}. Consequently, consistent and unbiased estimators for Gε (x) and GX (ε∗ ) are given by ˆ ε (x) = n−1 G ˆ X (ε∗ ) = n−1 G

n i=1 n

g(εi , x),

(5.12)

g(ε∗ , Xi ),

(5.13)

i=1

respectively. Because inﬂuence functions of RAL estimators for β are proportional to elements orthogonal to the nuisance space, if we knew Gε (x), GX (ε∗ ), and E{g(ε, X)}, then a natural estimator for β would be obtained by solving the estimating equation n

[g{εi (β), Xi } − Gε (Xi ) − GX {εi (β)} + E{g(ε, X)}] = 0q×1 ,

i=1

where εi (β) = Yi − µ(Xi , β). Since Gε (x), GX (ε∗ ), and E{g(ε, X)} are not known, a natural strategy for obtaining an estimator for β is to substitute estimates of these quantities in the preceding estimating equation, leading to the estimating equation

5.1 Location-Shift Regression Model n /

107

0 ˆ ε(β) (Xi ) − G ˆ X {εi (β)} + E[g{ε(β), ˆ g{εi (β), Xi } − G X}] = 0q×1 ,

i=1

(5.14) ˆ X {εi (β)} are deﬁned by (5.12) and (5.13), respectively, ˆ ε(β) (Xi ), G where G n ˆ and E[g{ε(β), X}] = n−1 i=1 g{εi (β), Xi }. The estimator for β that solves (5.14) should be a consistent and asymptotically normal semiparametric estimator for β with inﬂuence function proportional to (5.11). Rather than trying to prove the asymptotic properties of this estimator, we will instead focus on deriving a class of locally eﬃcient estimators for β and investigate the asymptotic properties of this class of estimators. Eﬃcient Score for the Location-Shift Regression Model The eﬃcient estimator for β has an inﬂuence function proportional to the eﬃcient score (i.e., the residual after projecting the score vector with respect to β onto the nuisance tangent space). To obtain the score vector with respect to β, we consider the density for a single observation (Y, X), which is given by pY,X (y, x, β) = pε {y − µ(x, β)}pX (x). Therefore, Sβq×1 (y, x) =

∂ log pY,X (y, x, β) ∂β

= −D

T q×1

where D(x, β0 ) =

β=β0

(x, β0 )Sε (ε),

∂µ(x, β0 ) ∂β T

and

∂ log pε (ε) . dε We ﬁrst prove that E{Sε (ε)} = 0. Sε (ε) =

(5.15)

Theorem 5.3. If the random variable ε is continuous with support on the real line, then E{Sε (ε)} = 0, where Sε (ε) is deﬁned by (5.15). Proof. Because ε is a continuous random variable whose distribution is dominated by Lebesgue measure and has support on the real line, this means that pε (ε − µ)dε = 1 for all scalar constants µ. This implies that d/dµ{ pε (ε − µ)dε} = 0 for all µ. Interchanging diﬀerentiation and integration, we obtain −pε (ε − µ)dε = 0 for all µ, where pε (x) = dp ε (x)/dx. Multiplying and dividing by pε (ε) and taking µ = 0, we obtain − Sε (ε)pε (ε)dε = 0, or E{Sε (ε)} = 0.

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5 Other Examples of Semiparametric Models

Because E{Sε (ε)} = 0, we use (5.11) to deduce that the eﬃcient score is given by Sβ (ε, X) − Π [Sβ |Λ] = −DT (X, β0 )Sε (ε) + E{DT (X, β0 )}Sε (ε) = −{DT (X, β0 ) − E{DT (X, β0 )}}Sε (ε).

(5.16)

If the distributions of X, pX (x), and ε, pε (ε), were known to us, then we would also know E{DT (X, β)} and Sε (ε). If this were the case, then (5.16) suggests ﬁnding the eﬃcient estimator for β by solving the equation n

[DT (Xi , β) − E{DT (X, β)}]Sε {Yi − µ(Xi , β)} = 0.

(5.17)

i=1

Note 1. The sign in (5.16) was reversed, but this is not important, as the estimator remains the same. However, since E{DT (X, β)} is not known to us, a natural strategy is to substitute an estimator for E{DT (X, β)} in (5.17), leading to the estimator for β that solves the estimating equation n

¯ T (β)}Sε {Yi − µ(Xi , β)} = 0, {DT (Xi , β) − D

(5.18)

i=1 n ¯ T (β) = n−1 DT (Xi , β). where D i=1

Locally Eﬃcient Adaptive Estimators The function Sε (ε) depends on the underlying density pε (ε), which is unknown to us. Consequently, we may posit some underlying density for ε to start with, some working density pε (ε) that may or may not be correct. Therefore, in what follows, we consider the asymptotic properties of the estimator for β, denoted by βˆn , which is the solution to equation (5.18) for an arbitrary function of ε, Sε (ε), which may not be a score function or, for that matter, may not have mean zero at the truth; i.e., E{Sε (ε)} = 0. To emphasize the fact that we may not be using the correct score function Sε (ε), we will substitute an arbitrary function of ε, which we denote by κ(ε), for Sε (ε) in equation (5.18). We now investigate (heuristically) the asymptotic properties of the estimator βˆn , which solves (5.18) for an arbitrary function κ(·) substituted for Sε (·). Theorem 5.4. The estimator βˆn , which is the solution to the estimating equation n ¯ T (β)}κ{Yi − µ(Xi , β)} = 0, {DT (Xi , β) − D (5.19) i=1

5.1 Location-Shift Regression Model

109

is asymptotically normal. That is, D n1/2 (βˆn − β0 ) −→ N (0, Σ),

where

Σ=E

∂κ(ε) ∂ε

−2

' (−1 var{κ(ε)} var DT (X, β0 ) .

Proof. To derive the asymptotic distribution of βˆn , we expand βˆn about β0 in equation (5.19) and, after multiplying by n−1/2 , we obtain 0 = n−1/2

n

¯ T (βˆn )}κ{Yi − µ(Xi , βˆn )} {DT (Xi , βˆn ) − D

i=1

= n−1/2

¯ T (β0 )}κ{Yi − µ(Xi , β0 )} {DT (Xi , β0 ) − D

(5.20)

i=1

" n ' ( ∂ ¯ T (β ∗ ) κ{Yi − µ(Xi , β ∗ )} DT (Xi , βn∗ ) − D (5.21) n−1 n n ∂β T i=1

+

n

n1/2 (βˆn − β0 ), where βn∗ is an intermediate value between βˆn and β0 . Consequently, n ( ∂ ' T 1/2 ˆ ¯ T (β ∗ ) n (βn − β0 ) = −n−1 D (Xi , βn∗ ) − D n T ∂β i=1 "−1 n ' T ( ¯ T (β0 ) D (Xi , β0 ) − D × n−1/2 κ{Yi − µ(Xi , βn∗ )} i=1

κ{Yi − µ(Xi , β0 )}.

(5.22)

Under suitable regularity conditions, the sample average (5.21) will converge in probability to (q×1 ∂κ(ε) ' T T 1×q D(X, β0 ) (5.23) E D (X, β0 ) − E{D (X, β0 )} ∂ε ∂ ∂ +E DT (X, β0 ) − E DT (X, β0 ) κ(ε) . (5.24) ∂β T ∂β T Because of the independence of ε and X, (5.23) is equal to ' ( ∂κ(ε) var DT (X, β0 ) , E ∂ε where var{DT (X, β0 )} = E{DT (X, β0 )D(X, β0 )} − E{DT (X, β0 )}E{D(X, β0 )}

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5 Other Examples of Semiparametric Models

is the variance matrix of DT (X, β0 ), and (5.24) = 0. Therefore (5.22) can be written as ' T ( −1 ∂κ(ε) 1/2 ˆ var D (X, β0 ) n (βn − β0 ) = −E ∂ε n ' T ( ¯ T (β0 ) κ(εi ) + op (1). (5.25) D (Xi , β0 ) − D n−1/2 i=1

Note that n−1/2

n '

( ¯ T (β0 ) κ(εi ) DT (Xi , β0 ) − D

i=1

= n−1/2

n '

( ¯ T (β0 ) {κ(εi ) − κ DT (Xi , β0 ) − D ¯}

i=1

= n−1/2

n '

( DT (Xi , β0 ) − µD {κ(εi ) − µκ }

i=1

' T ( ¯ (β0 ) − µD {¯ κ − µκ } + n1/2 D n ' ( = n−1/2 DT (Xi , β0 ) − µD {κ(εi ) − µκ } + op (1),

(5.26)

i=1

where κ ¯ = n−1

n

' ( κ(εi ), µD = E DT (Xi , β0 ) , and µκ = E {κ(εi )} .

i=1

Therefore, by (5.25) and (5.26), we obtain that ' ( −1 dκ(ε) var DT (X, β0 ) n1/2 (βˆn − β0 ) = −E dε n ' T ( −1/2 D (Xi , β0 ) − µD {κ(εi ) − µκ } + op (1). ×n i=1

Consequently, the inﬂuence function of βˆn is ' T ( ( −1 ' T dκ(ε) var D (X, β0 ) D (Xi , β0 ) − µD {κ(εi ) − µκ } , −E ∂ε (5.27) 1/2 ˆ and n (βn − β0 ) is asymptotically normal with mean zero and variance matrix equal to the variance matrix of the inﬂuence function, which equals −2 ' (−1 ∂κ(ε) var{κ(ε)} var DT (X, β0 ) . E ∂ε

5.1 Location-Shift Regression Model

111

Remark 1. If we start with an arbitrary function g(ε, X) = DT (X, β0 )κ(ε) regardless of whether κ(ε) = Sε (ε) = ∂ log then by (5.11),

pε (ε) ∂ε

or some other function of ε,

DT (X, β0 )κ(ε) − DT (X, β0 )µκ − µD κ(ε) + µD µκ ( ' T = D (X, β0 ) − µD {κ(ε) − µκ } is orthogonal to the nuisance tangent space. We also notice that this is proportional to the inﬂuence function of βˆn given by (5.27). If, however, we choose the true density pε (ε), then we obtain an eﬃcient estimator. Consequently, the estimator for β given by (5.19) is a locally eﬃcient semiparametric estimator for β. If we wanted to derive a globally eﬃcient semiparametric estimator, then we would need to estimate the score function ∂ log pε (ε)/∂ε nonparametrically and substitute this for κ(ε) in the estimating equation (5.19). Although this may be theoretically possible (see Bickel et al., 1993, Section 7.8), generally such nonparametric estimators are unstable (unless the sample size is very large). Another strategy would be to posit some parametric model, say pε (ε, ξ), for the density of ε in terms of a ﬁnite number of parameters ξ. The parameter ξ can be estimated, say, by using pseudo-likelihood techniques; i.e., by maximizing the pseudo-likelihood, n !

pε {Yi − µ(Xi , βˆnI ), ξ},

i=1

as a function in ξ using some initial consistent estimator βˆnI of β. Letting ξˆn denote such an estimator, we can estimate β by substituting Sε (ε, ξˆn ) = ∂ log pε (ε, ξˆn )/∂ε into equation (5.18). Such an adaptive estimator is locally eﬃcient in the sense that if the true density of ε is an element of the posited parametric model, then the resulting estimator is eﬃcient; otherwise, it will still be a consistent asymptotically normal semiparametric estimator for β. The idea here is that with a ﬂexible parametric model, we can get a reasonably good approximation for the underlying density of ε, so even if we don’t estimate this density consistently, it would hopefully be close enough so that the resulting estimator will have good eﬃciency properties. In the following example, we consider the linear model and use this to contrast the estimators obtained for the restricted moment model and the location-shift regression model. Example 1. Consider the linear model (5.1) Yi = XiT β + εi ,

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5 Other Examples of Semiparametric Models

where Yi is a one-dimensional random variable. For this model, DT (Xi , β) = Xiq×1 . The estimator for β given by (5.18) is the solution to n

¯ ε (Yi − XiT β) = 0. (Xi − X)S

(5.28)

i=1

Suppose we believe our data are normally distributed; i.e., εi ∼ N (µε , σ 2 ) or 1 1 2 pε (ε) = √ exp − 2 (ε − µε ) . 2σ 2πσ 2

Then Sε (ε) = −

ε − µε σ2

.

Substituting into (5.28) yields n

T ¯ (Yi − Xi β − µε ) = 0. (Xi − X) σ2 i=1

Since σ 2 is a multiplicative constant and for β is equivalent to solving n

n

i=1 (Xi

¯ = 0, the estimator − X)

¯ i − X T β) = 0. (X1 − X)(Y i

i=1

This gives the usual least-squares estimator for the regression coeﬃcients in a linear model with an intercept term; namely, βˆn =

n i=1

"−1 ¯ ¯ T (Xi − X)(X i − X)

n

¯ i. (Xi − X)Y

(5.29)

i=1

We mentioned previously that the location-shift regression model is contained within the restricted moment model Yi = α + β1 Xi1 + · · · + βq Xiq + εi , where E(εi |Xi ) = 0. For the location-shift regression model, εi is independent of Xi , which implies that the variance function V (Xi ) = var (Yi |Xi ) = σ 2 (a constant independent of Xi ). The eﬃcient estimator for β among semiparametric estimators for β in the restricted moment model, given by (4.54), is

5.2 Proportional Hazards Regression Model with Censored Data n

113

DT (Xi )V −1 (Xi ){Yi − µ(Xi , β)} = 0.

i=1

If V (Xi ) is assumed constant, then the eﬃcient restricted moment estimator for (α, β T )T is given as the solution to σ −2

n

(1, XiT )T (Yi − α − XiT β) = 0.

i=1

Some usual matrix algebra yields βˆn =

n

"−1 ¯ ¯ (Xi − X)(X i − X)

T

i=1

n

¯ i, (Xi − X)Y

i=1

which is identical to the estimator (5.29), the locally eﬃcient estimator for β in the location-shift regression model.

Remarks 1. The estimator βˆn given by (5.29) is the eﬃcient estimator for β among semiparametric estimators for the location-shift regression model if the error distribution is indeed normally distributed. 2. If, however, the error distribution was not normally distributed, other semiparametric estimators (semiparametric for the location-shift model) would be more eﬃcient; namely, the solution to the equation n

¯ ε (Yi − X T β) = 0, (Xi − X)S i

(5.30)

i=1

where Sε (ε) = d logdεpε (ε) . 3. In contrast, among the estimators for β for the restricted moment model, when the variance function V (Xi ) is assumed constant, the eﬃcient estimator for β is given by (5.29). 4. The estimator for β given by (5.30) may not be consistent and asymptotically normal if εi is not independent of Xi but E(εi |Xi ) = 0, whereas (5.29) is.

5.2 Proportional Hazards Regression Model with Censored Data The proportional hazards model, which was introduced by Cox (1972), is the most widely used model for analyzing survival data. This model is especially

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5 Other Examples of Semiparametric Models

useful for survival data that are right censored, as is often the case in many clinical trials where the primary endpoint is “time to an event” such as time to death, time to relapse, etc. As we will see shortly, the proportional hazards model is a semiparametric model that can be represented naturally with a ﬁnite number of parameters of interest and an inﬁnite-dimensional nuisance parameter. Because of this natural representation with a parametric and nonparametric component, it was one of the ﬁrst models to be studied using semiparametric theory, in the landmark paper of Begun et al. (1983). In order to follow the arguments in this section, the reader must be familiar with the theory of counting processes and associated martingale processes that have been developed for studying the theoretical properties of many statistics used in censored survival analysis. Some excellent books for studying counting processes and their application to censored survival-data models include those by Fleming and Harrington (1991) and Andersen et al. (1992). The reader who has no familiarity with this area can skip this section, as it is self-contained and will not aﬀect the understanding of the remainder of the book. The primary goal of the proportional hazards model is to model the relationship of “time to event” as a function of a vector of covariates X. Throughout this section, we will often refer to the “time to event” as the “survival time” since, in many applications where these models are used, the primary endpoint is time to death. But keep in mind that the time to any event could be used. Let T denote the underlying survival time of an arbitrary individual in our population. The random variable T will be assumed to be a continuous positive random variable. Unlike previous models where we considered the conditional mean of the response variable, or shifts in the location of the distribution of the response variable as a function of covariates, in the proportional hazards model it is the hazard rate of failure that is modeled as a function of covariates X (q-dimensional). Speciﬁcally, the proportional hazards model of Cox (1972) assumes that P (u ≤ T < u + h|T ≥ u, X) λ(u|X) = lim h→0 h = λ(u) exp(β T X),

(5.31)

where λ(u|x) is the conditional hazard rate of failure at time u given X = x. The baseline hazard function λ(u) can be viewed as the underlying hazard rate if all the covariates X are equal to zero and this underlying hazard rate is left unspeciﬁed (nonparametric). This baseline hazard rate is assumed to be increased or decreased proportionately (the same proportionality constant through time) as a function of the covariates X through the relationship exp(β T X). Consequently, the parameters β in this model measure the strength of the association of this proportionality increase or decrease in the hazard rate as a function of the covariates, and it is these parameters that will be of primary interest to us.

5.2 Proportional Hazards Regression Model with Censored Data

115

In many experimental settings where survival data are obtained, such as in clinical trials, not all the survival data are available for the individuals in the study. Some of the survival data may be right censored. That is, for some individuals, we may only know that they survived to some time. For example, in a clinical trial where patients enter the study during some accrual period and where the study is analyzed before all the patients die, a patient who has not died has a survival time that is right censored; that is, we only know that this patient survived the period of time between their entry into the study and the time the study was analyzed. This is an example of what is referred to as administrative censoring. Other reasons for censoring include patient dropout, where we only know that a patient was still alive at the time they dropped out. To accommodate censoring, we deﬁne the random variable C that corresponds to the potential time that an individual is followed for survival. We denote the conditional density of C given X as pC|X (c|x). In this setting, we observe, for each individual, the variables V = min(T, C) : (time on study), ∆ = I(T ≤ C) : (failure indicator), and X = q-dimensional covariate vector. In addition, we assume that T and C are conditionally independent given X. This is denoted as T ⊥ ⊥ C|X. This assumption is necessary to allow for the identiﬁability of the conditional distribution of T given X when we have censored data; see Tsiatis (1998) for more details. Otherwise, no other assumptions are made on the conditional distribution of C given X. The data from a survival study are represented as (Z1 , . . . , Zn ), iid, where Zi = (Vi , ∆i , Xi ), i = 1, . . . , n. The goal is to ﬁnd semiparametric consistent, asymptotically normal, and efﬁcient estimators for β using the sample (Vi , ∆i , Xi ), i = 1, . . . , n without making any additional assumptions on the underlying baseline hazard function λ(u), on the conditional distribution of C given X, or on the distribution of X. To do so, we will use the semiparametric theory that we have developed. Speciﬁcally, we will ﬁnd the space of inﬂuence functions of RAL estimators for β for this model, which, in turn, will motivate a class of RAL estimators among which we will derive the eﬃcient estimator. This will be accomplished by deriving the semiparametric nuisance tangent space, its orthogonal complement, where inﬂuence functions lie, and the eﬃcient score. We begin by ﬁrst considering the density of the observable data. The density of a single data item is given by ' (δ ' ( pV,∆,X (v, δ, x) = λ(v) exp(β T x) exp −Λ(v) exp(β T x) ⎧∞ ⎫δ ⎬ ' (1−δ ⎨ pC|X (v|x) pC|X (u|x)du pX (x), (5.32) ⎩ ⎭ v

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5 Other Examples of Semiparametric Models

where the cumulative baseline hazard function is deﬁned as v Λ(v) =

λ(u)du, 0

and pX (x) denotes the marginal density of X. It will be convenient to use a hazard representation for the conditional distribution of C given X. Deﬁne P (u ≤ C < u + h|C ≥ u, X = x) λC|X (u|x) = lim h→0 h and

v ΛC|X (v|x) =

λC|X (u|x)du. 0

Using the fact that pC|X (v|x) = λC|X (v|x) exp{−ΛC|X (v|x)} and

∞

pC|X (u|x)du = exp{−ΛC|X (v|x)}, v

we write the density (5.32) as ' (δ ' ( pV,∆,X (v, δ, x) = λ(v) exp(β T x) exp −Λ(v) exp(β T x) ' (1−δ ' ( × λC|X (v|x) exp −ΛC|X (v|x) × pX (x).

(5.33)

The model is characterized by the parameter (β, η), where β denotes the qdimensional regression parameters of interest and the nuisance parameter η = {λ(v), λC|X (v|x), pX (x)}, where λ(v) is an arbitrary positive function of v, λC|X (v|x) is an arbitrary positive function of v and x, and pX (x) is any density such that pX (x)dνX (x) = 1. The log of the density in (5.33) is ' ( δ log λ(v) + β T x − Λ(v) exp(β T x) + (1 − δ) log λC|X (v|x) − ΛC|X (v|x) + log pX (x).

(5.34)

5.2 Proportional Hazards Regression Model with Censored Data

117

The Nuisance Tangent Space For this problem, the Hilbert space H consists of all q-dimensional measurable functions h(v, δ, x) of (V, ∆, X) with mean zero and ﬁnite variance equipped with the covariance inner product. In order to deﬁne the nuisance tangent space within the Hilbert space H, we must derive the mean-square closure of all parametric submodel nuisance tangent spaces. Toward that end, we deﬁne an arbitrary parametric submodel by substituting λ(v, γ1 ), λC|X (v|x, γ2 ) and pX (x, γ3 ) into (5.33), where γ1 , γ2 , and γ3 are ﬁnite-dimensional nuisance parameters of dimension r1 , r2 , and r3 , respectively. Since the nuisance parameters γ1 , γ2 , and γ3 are variationally independent and separate from each other in the log-likelihood of (5.34), this implies that the parametric submodel nuisance tangent space, and hence the nuisance tangent space, can be written as a direct sum of three orthogonal spaces, Λ = Λ1s ⊕ Λ2s ⊕ Λ3s , where Λjs = mean-square closure of {B q×rj Sγj (V, ∆, X) for all B q×rj }, j = 1, 2, 3, and Sγj (v, δ, x) = ∂ log pV,∆,X (v, δ, x, γ)/∂γj are the parametric submodel score vectors, for j = 1, 2, 3. Since the space Λ3s is associated with arbitrary marginal densities pX (x) of X, we can use arguments already developed in Chapter 4 to show that ' ( (5.35) Λ3s = αq×1 (X) : E αq×1 (X) = 0q×1 and that the projection of an arbitrary element h(V, ∆, X) ∈ H is given by Π(h|Λ3s ) = E(h|X). We will now show in a series of lemmas how to derive Λ1s and Λ2s followed by the key theorem that derives the space orthogonal to the nuisance tangent space; i.e., Λ⊥ . The Space Λ2s Associated with λC|X (v|x) Using counting process notation, let NC (u) = I(V ≤ u, ∆ = 0) denote (count) the indicator of whether a single individual is observed to be censored by or at time u and Y (u) = I(V ≥ u) denote the indicator of being at risk at time u. The corresponding martingale increment is dMC (u, x) = dNC (u) − λ0C|X (u|x)Y (u)du, where λ0C|X (u|x) denotes the true conditional hazard function of C at time u given X = x.

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5 Other Examples of Semiparametric Models

Lemma 5.1. The space Λ2s associated with the arbitrary conditional density of C given X is given as the class of elements q×1 q×1 α (u, X)dMC (u, X) for all functions α (u, x) , where αq×1 (u, x) is any arbitrary q-dimensional function of u and x. Proof. In order to derive the space Λ2s , consider the parametric submodel λC|X (v|x, γ2 ) = λ0C|X (v|x) exp{γ2T αq×1 (v, x)}. That this is a valid parametric submodel follows because the truth is contained within this model (i.e., when γ2 = 0) and a hazard function must be some positive function of v. The contribution to the log-likelihood (5.34) for this parametric submodel is ' ( (1 − δ) log λ0C|X (v|x) + γ2T α(v, x) −

v

' ( λ0C|X (u|x) exp γ2T α(u, x) du.

0

Taking the derivative with respect to γ2 and evaluating it at the truth (γ2 = 0), we obtain the nuisance score vector V Sγ2 = (1 − ∆)α(V, X) −

α(u, X)λ0C|X (u|X)du 0

= (1 − ∆)α(V, X) −

α(u, X)λ0C|X (u|X)I(V ≥ u)du.

Using counting process notation, Sγ2 can be written as a stochastic integral, Sγ2 = α(u, X)dMC (u, X). From this last result, we conjecture that the space Λ2s consists of all elements in the class αq×1 (u, X)dMC (u, X) for all functions αq×1 (u, x) . We have already demonstrated that any element in the class above is an element of a parametric submodel nuisance tangent space. Therefore, to complete our argument and verify our conjecture, we need to show that the linear space spanned by the score vector with respect to γ2 for any parametric submodel belongs to the space above. Consider any arbitrary parametric submodel λC|X (u|x, γ2 ), with γ20 denoting the truth. The score vector is given by

5.2 Proportional Hazards Regression Model with Censored Data

∂ ∂γ2

= (1 − ∆) − =

(1 − ∆) log λC|X (V |x, γ2 ) − ∂ ∂γ2 λC|X (V

119

λC|X (u|X, γ2 )I(V ≥ u)du

γ2 =γ20

|X, γ20 )

λC|X (V |X, γ20 )

∂ ∂γ2 λC|X (u|X, γ20 )

λC|X (u|X, γ20 )

λC|X (u|X, γ20 )I(V ≥ u)du

∂ log λC|X (u|X, γ20 ) ∂γ2

dMC (u, X).

Multiplying this score vector by a conformable matrix results in an element of the form α(u, X)dMC (u, X). The Space Λ1s Associated with λ(v) Let N (u) denote the counting process that counts whether an individual was observed to die before or at time u (i.e., N (u) = I(V ≤ u, ∆ = 1)) and, as before, Y (u) = I(V ≥ u) is the “at risk” indicator at time u. Let dM (u, X) denote the martingale increment dN (u) − λ0 (u) exp(β0T X)Y (u)du, where λ0 (u) denotes the true underlying hazard rate in the proportional hazards model. Lemma 5.2. The space Λ1s , the part of the nuisance tangent space associated with the nuisance parameter λ(·), the underlying baseline hazard rate of failure, is q×1 q×1 a (u)dM (u, X) for all q-dimensional functions a (u) , Λ1s = where aq×1 (u) denotes an arbitrary q-dimensional function of u. Proof. Consider the parametric submodel λ(v, γ1 ) = λ0 (v) exp{γ1T aq×1 (v)} for any arbitrary q-dimensional function aq×1 (v) of v. For this parametric submodel, the contribution to the log-density is v δ{log λ0 (v) + γ1T a(v) + β T x} − λ0 (u) exp{γ1T a(u) + β T x}du. (5.36) 0

Taking derivatives of (5.36) with respect to γ1 , setting γ1 = 0 and β = β0 , we obtain the score function Sγ1 = aq×1 (u)dM (u, X).

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5 Other Examples of Semiparametric Models

From this, we conjecture that Λ1s is q×1 q×1 a (u)dM (u, X) for all q-dimensional functions a (u) . Λ1s = We have already demonstrated that any element in Λ1s is an element of a parametric submodel nuisance tangent space. Therefore, to complete our argument and verify our conjecture, we need to show that the linear space spanned by the score vector with respect to γ1 for any parametric submodel belongs to Λ1s . Consider the parametric submodel with the nuisance parameter γ1 , which appears in the log-density (5.34) as ' ( δ log λ(v, γ1 ) + β T x − Λ(v, γ1 ) exp(β T x), (5.37) v where Λ(v, γ1 ) = 0 λ(u, γ1 )du and γ10 denotes the truth; i.e., λ(v, γ10 ) = λ0 (v). Taking the derivative of (5.37) with respect to γ1 , setting γ1 = γ10 and β = β0 , we deduce that the score vector with respect to γ1 is ∂ log λ(u, γ10 ) dM (u, X). S γ1 = ∂γ1 Multiplying this score vector by a conformable matrix leads to an element in Λ1s , thus verifying our conjecture. Finding the Orthogonal Complement of the Nuisance Tangent Space We have demonstrated that the nuisance tangent space can be written as a direct sum of three orthogonal linear spaces, namely Λ = Λ1s ⊕ Λ2s ⊕ Λ3s , where Λ2s and Λ1s were derived in Lemmas 5.1 and 5.2, respectively, and Λ3s is given in (5.35). Inﬂuence functions of RAL estimators for β belong to the space orthogonal to Λ. We now consider ﬁnding the orthogonal complement to Λ. Theorem 5.5. The space orthogonal to the nuisance tangent space is given by ⊥ ∗ q×1 {α(u, X) − a (u)} dM (u, X) for all α (u, x) , (5.38) Λ = where

' ( E α(u, X) exp(β0T X)Y (u) ' ( . a (u) = E exp(β0T X)Y (u) ∗

(5.39)

5.2 Proportional Hazards Regression Model with Censored Data

121

Proof. We begin by noting some interesting geometric properties for the nuisance tangent space that we can take advantage of in deriving its orthogonal complement. If we put no restrictions on the densities pV,∆,X (v, δ, x) that generate our data (completely nonparametric), then it follows from Theorem 4.4 that the corresponding tangent space would be the space of all q-dimensional measurable functions of (V, ∆, X) with mean zero; i.e., the entire Hilbert space H. The proportional hazards model we are considering puts no restrictions on the marginal distribution of X, pX (x) or the conditional distribution of C given X. Therefore, the only restrictions on the class of densities for (V, ∆, X) come from those imposed on the conditional distribution of T given X via the proportional hazards model. Suppose, for the time being, we put no restriction on the conditional hazard of T given X and denote this by λT |X (v|x). If this were the case, then there would be no restrictions on the distribution of (V, ∆, X). The distribution of (V, ∆, X) given by the density PV,∆,X (v, δ, x) can be written as PV,∆|X (v, δ|x)pX (x), where the conditional density PV,∆|X (v, δ|x) can also be characterized through the cause-speciﬁc hazard functions P (v ≤ V < v + h, ∆ = δ|V ≥ v, X = x) ∗ , λ∆ (v|x) = lim h→0 h for ∆ = 0, 1. Under the assumption T ⊥ ⊥ C|X, the cause-speciﬁc hazard functions equal the net-speciﬁc hazard functions; namely, λ∗1 (v|x) = λT |X (v|x), λ∗0 (v|x) = λC|X (v|x).

(5.40)

That (5.40) is true follows from results in Tsiatis (1998). Therefore, putting no restrictions on λT |X (v|x) or λC|X (v|x) implies that no restrictions are placed on the conditional distribution of (V, ∆) given X. Hence, the log-density for such a saturated (nonparametric) model could be written (analogously to (5.34)) as δ log λT |X (v|x) − ΛT |X (v|x) + (1 − δ) log λC|X (v|x) − ΛC|X (v|x) + log pX (x). The tangent space for this model can be written as a direct sum of three orthogonal spaces, Λ∗1s ⊕ Λ2s ⊕ Λ3s , where Λ2s and Λ3s are deﬁned as before, but now Λ∗1s is the space associated with λT |X (v|x), which is now left arbitrary. Arguments that are virtually identical to those used to ﬁnd the space Λ2s in Lemma 5.1 can be used to show that

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5 Other Examples of Semiparametric Models

Λ∗1s =

αq×1 (u, X)dM (u, X) for all αq×1 (u, x) ,

where dM (u, X) = dN (u) − λ0T |X (u|X)Y (u)du. Note 2. Notice the diﬀerence: For Λ1s we used aq×1 (u) (i.e., a function of u only), whereas for Λ∗1s we used αq×1 (u, X) (i.e., a function of both u and X) in the stochastic integral above. Because the tangent space Λ∗1s ⊕ Λ2s ⊕ Λ3s is that for a nonparametric model (i.e., a model that allows for all densities of (V, ∆, X)), and because the tangent space for a nonparametric model is the entire Hilbert space, this implies that H = Λ∗1s ⊕ Λ2s ⊕ Λ3s , (5.41) where Λ∗1s , Λ2s , and Λ3s are mutually orthogonal subspaces. Since the nuisance tangent space Λ = Λ1s ⊕ Λ2s ⊕ Λ3s , this implies that Λ1s ⊂ Λ∗1s . Also, the orthogonal complement Λ⊥ must be orthogonal to Λ2s ⊕ Λ3s = Λ∗1s ; i.e., Λ⊥ ⊂ Λ∗1s . Λ⊥ must also be orthogonal to Λ1s ; consequently, Λ⊥ consists of elements of Λ∗1s that are orthogonal to Λ1s . In order to identify elements of Λ⊥ (i.e., elements of Λ∗1s that are orthogonal to Λ1s ), it suﬃces to take an arbitrary element of Λ∗1s , namely αq×1 (u, X)dM (u, X) and ﬁnd its residual after projecting it onto Λ1s . To ﬁnd the projection, we must derive a∗ (u) so that α(u, X)dM (u, X) − a∗ (u)dM (u, X) is orthogonal to every element of Λ1s . That is, T ∗ E {α(u, X) − a (u)} dM (u, X) a(u)dM (u, X) = 0 for all a(u). The covariance of martingale stochastic integrals such as those above can be computed by ﬁnding the expectation of the predictable covariation process; see Fleming and Harrington (1991). Namely, T ∗ T E {α(u, X) − a (u)} a(u)λ0 (u) exp(β0 X)Y (u)du (5.42) T = E {α(u, X) − a∗ (u)} exp(β0T X)Y (u) λ0 (u)a(u)du = 0 (5.43)

5.2 Proportional Hazards Regression Model with Censored Data

for all a(u). Since a(u) is arbitrary, this implies that T E {α(u, X) − a∗ (u)} exp(β0T X)Y (u) = 0q×1

123

(5.44)

for all u. We can prove (5.44) by contradiction because if (5.44) was not equal to zero, then we could make the integral (5.43) nonzero by choosing a(u) to be equal to whatever the expectation is in (5.44). Solving (5.44), we obtain ' ( ' ( E α(u, X) exp(β0T X)Y (u) = a∗ (u)E exp(β0T X)Y (u) ( ' E α(u, X) exp(β0T X)Y (u) ' ( . a (u) = E exp(β0T X)Y (u)

or

∗

Therefore, the space orthogonal to the nuisance tangent space is given by Λ⊥ = {α(u, X) − a∗ (u)} dM (u, X) for all αq×1 (u, x) , ' ( E α(u, X) exp(β0T X)Y (u) ' ( a (u) = . E exp(β0T X)Y (u)

where

∗

Finding RAL Estimators for β As we have argued repeatedly, inﬂuence functions of RAL estimators for β are deﬁned, up to a proportionality constant, through the elements orthogonal to the nuisance tangent space. For the proportional hazards model, these elements are deﬁned through (5.38) and (5.39). Often, knowing the functional form of such elements leads us to estimating equations that will result in RAL estimators for β with a particular inﬂuence function. We now illustrate. Since a∗ (u), given by (5.39), is deﬁned through a ratio of expectations, it is natural to estimate this using a ratio of sample averages, namely n n−1 i=1 α(u, Xi ) exp(β0T Xi )Yi (u) n . a ˆ∗ (u) = n−1 i=1 exp(β0T Xi )Yi (u) Note that n−1/2

n

{α(u, Xi ) − a∗ (u)} dMi (u, Xi )

i=1

= n−1/2

n i=1

This follows because

{α(u, Xi ) − a ˆ∗ (u)} dMi (u, Xi ) + op (1).

(5.45)

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5 Other Examples of Semiparametric Models

n

−1/2

n

{ˆ a∗ (u) − a∗ (u)} dMi (u, Xi ) − → 0, P

i=1

a consequence of Lenglart’s inequality; see Fleming and Harrington (1991). Using straightforward algebra, we obtain that ⎧ ⎫ n T ⎪ ⎪ α(u, X ) exp(β X )Y (u) ⎪ ⎪ j j j 0 n ⎨ ⎬ j=1 α(u, Xi ) − dMi (u, Xi ) n ⎪ ⎪ * +, ⎪ ⎪ i=1 exp(β0T Xj )Yj (u) ⎩ ⎭ || j=1 dNi (u) − λ0 (u) exp(β0T Xi )Yi (u) is identical to ⎧ ⎫ n T ⎪ ⎪ α(u, X ) exp(β X )Y (u) ⎪ ⎪ j j j 0 n ⎨ ⎬ j=1 α(u, Xi ) − dNi (u). n ⎪ ⎪ ⎪ ⎪ i=1 exp(β0T Xj )Yj (u). ⎩ ⎭ j=1

Using standard expansions of the estimating equation (which we leave to the reader as as exercise), where we expand the estimating equation about the truth, we can show that the estimator for β, which is the solution to the estimating equation

n

α(u, Xi ) −

i=1

α(u, Xj ) exp(β T Xj )Yj (u) exp(β T Xj )Yj (u)

dNi (u) = 0,

will have an inﬂuence function “proportional” to the element of Λ⊥ , {α(u, Xi ) − a∗ (u)} dMi (u, Xi ). That is, we can ﬁnd a semiparametric estimator for β by choosing any qdimensional function α(u, x) of u and x and solving the estimating equation ⎧ ⎫ n T ⎪ ⎪ α(V , X ) exp(β X )Y (V ) ⎪ ⎪ i j j j i ⎨ ⎬ j=1 = 0. ∆i α(Vi , Xi ) − n ⎪ ⎪ ⎪ ⎪ T i=1 exp(β Xj )Yj (Vi ) ⎩ ⎭

n

(5.46)

j=1

By considering all functions αq×1 (u, x), the corresponding estimators will deﬁne a class of semiparametric estimators that contains all the inﬂuence functions of RAL estimators for β.

5.3 Estimating the Mean in a Nonparametric Model

125

Eﬃcient Estimator To ﬁnd the eﬃcient estimator, we must derive the eﬃcient score. This entails computing Sβ (Vi , ∆i , Xi ) and projecting this onto the nuisance tangent space. Going back to the logdensity (5.34) and taking the derivative with respect to β, it is straightforward to show that Sβ = X q×1 dM (u, X). We note that Sβ is an element of Λ∗1s , with α(u, Xi ) = Xi . Therefore, the eﬃcient score, derived as the residual after projecting Sβ onto Λ (or in this case Λ1s ), is given as E{X exp(β0T X)Y (u)} Seﬀ = dM (u, X). X− E{exp(β0T X)Y (u)} The estimator for β, which has an eﬃcient inﬂuence function (i.e., proportional to Seﬀ ), is given by substituting Xi for α(u, Xi ) in (5.46); namely, ⎫⎤ ⎧ n ⎪ ⎪ X exp(βX )Y (V ) ⎪ ⎪ j j j i ⎬⎥ ⎨ ⎢ j=1 ⎥ = 0. X ∆i ⎢ − n ⎣ i ⎪ ⎦ ⎪ ⎪ ⎪ i=1 exp(βXj )Yj (Vi ) ⎭ ⎩ ⎡

n

(5.47)

j=1

The estimator for β, given as the solution to (5.47), is the estimator proposed by Cox for maximizing the partial likelihood, where the notion of partial likelihood was ﬁrst introduced by Cox (1975). The martingale arguments above are essentially those used by Andersen and Gill (1982), where the theoretical properties of the proportional hazards model are derived in detail. The argument above shows that Cox’s maximum partial likelihood estimator is a globally eﬃcient semiparametric estimator for β for the proportional hazards model.

5.3 Estimating the Mean in a Nonparametric Model Up to this point, we have identiﬁed inﬂuence functions and estimators by considering elements in the Hilbert space H that are orthogonal to the nuisance tangent space Λ. In Theorems 3.4 and 4.3, we also deﬁned the space of inﬂuence functions of RAL estimators for β for parametric and semiparametric models, respectively, by considering the linear variety ϕ(Z) + T ⊥ , where ϕ(Z) is any inﬂuence function of an RAL estimator for β and T is the tangent space. For some problems, this representation of inﬂuence functions may be more useful in identifying semiparametric estimators, including the eﬃcient

126

5 Other Examples of Semiparametric Models

estimator; i.e., when the parameter of interest is represented as β(θ), some function of the parameter θ, where the inﬁnite-dimensional parameter θ describes the entire parameter space, when the tangent space T is easier to derive, and when some simple semiparametric but (possibly) ineﬃcient RAL estimator, with inﬂuence function ϕ(Z), exists. We will illustrate this with two examples. In the ﬁrst example, we consider the problem of ﬁnding estimators for the mean of a random variable Z (i.e., µ = E(Z)) with a sample of iid random variables Z1 , . . . , Zn with common density p(z) with respect to some dominating measure νZ , where p(z) could be any density (i.e., p(z) can be any positive function of z such that p(z)dν(z) = 1 and µ = zp(z)dν(z) < ∞); that is, the so-called nonparametric model for the distribution of Z. Goal. We want to ﬁnd the class of RAL semi(non)parametric estimators for µ = E(Z). Basically, the model above puts no restriction on the density of Z other than ﬁnite moments. This is referred to as the nonparametric model. In Theorem 4.4, we proved that the tangent space T was the entire Hilbert space H. Consequently, T ⊥ , the orthogonal complement of T , is the single “zero” element of the Hilbert space corresponding to the origin. For this model, the space of inﬂuence functions, ϕ(Z) + T ⊥ , consists of, at most, one inﬂuence function, and if an inﬂuence function exists for an RAL estimator for µ, then this inﬂuence function must be the eﬃcient inﬂuence function and the corresponding estimator has to be semiparametric eﬃcient. An obvious and natural RAL estimator for µ = E(Z) is the sample mean n ¯ Z = n−1 Zi . This estimator can be trivially shown to be an RAL estimator i=1

for µ with inﬂuence function (Z−µ0 ). Therefore, Z−µ0 is the unique and hence eﬃcient inﬂuence function among RAL estimators for µ for the nonparametric model and Z¯ is the semiparametric eﬃcient estimator. We now consider the more complicated problem where the primary objective is to estimate the treatment eﬀect in a randomized study with covariate adjustment.

5.4 Estimating Treatment Diﬀerence in a Randomized Pretest-Posttest Study or with Covariate Adjustment The randomized study is commonly used to compare the eﬀects of two treatments on response in clinical trials and other experimental settings. In this design, patients are randomized to one of two treatments with probability δ or 1 − δ, where the randomization probability δ is chosen by the investigator. Together with the response variable, other baseline covariate information is

5.4 Estimating Treatment Diﬀerence in a Randomized Pretest-Posttest Study

127

collected on all the individuals in the study. The goal is to estimate the diﬀerence in the mean response between the two treatments. One simple estimator for this treatment diﬀerence is the diﬀerence in the treatment-speciﬁc sample average response between the two treatments. A problem that has received considerable interest is whether and how we can use the baseline covariates that are collected prior to randomization to increase the eﬃciency of the estimator for treatment diﬀerence. Along the same line, we also consider the randomized pretest-posttest design. In this design, a random sample of subjects (patients) are chosen from some population of interest and, for each patient, a pretest measurement, say Y1 , is made, and then the patient is randomized to one of two treatments, which we denote by the treatment indicator A = (1, 0) with probability δ and (1 − δ), and after some prespeciﬁed time period, a posttest measurement Y2 is made. The goal of such a study is to estimate the eﬀect of treatment intervention on the posttest measurement, or equivalently to estimate the eﬀect of treatment on the change score, which is the diﬀerence between the pretest response and posttest response. We will focus the discussion on the pretest-posttest design. However, we will see later that the results derived for the pretest-posttest study can be generalized to the problem of covariate adjustment in a randomized study. As an example of a pretest-posttest study, suppose we wanted to compare the eﬀect of some treatment intervention on the quality of life for patients with some disease. We may be interested in comparing the eﬀect that a new treatment has to a placebo or comparing a new treatment to the current best standard treatment. Speciﬁcally, such a design is carried out by identifying a group of patients with disease that are eligible for either of the two treatments. These patients are then given a questionnaire to assess their baseline quality of life. Typically, in these studies, patients answer several questions, each of which is assigned a score (predetermined by the investigator), and the quality of life score is a sum of these scores, denoted by Y1 . Afterward, they are randomized to one of the two treatments A = (0, 1) and then followed for some period of time and asked to complete the quality of life questionnaire again, where their quality of life score Y2 is computed. The goal in such studies is to estimate the treatment eﬀect, deﬁned as β = E(Y2 |A = 1) − E(Y2 |A = 0), or equivalently β = E(Y2 − Y1 |A = 1) − E(Y2 − Y1 |A = 0), where Y2 − Y1 is the diﬀerence from baseline in an individual’s response and is sometimes referred to as the change score. Note 3. This last equivalence follows because of randomization which guarantees that Y1 and A are independent; i.e., E(Y1 |A = 1) = E(Y1 |A = 0).

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5 Other Examples of Semiparametric Models

The data from such a randomized pretest-posttest study can be represented as Zi = (Y1i , Ai , Y2i ), i = 1, . . . , n. Some estimators for β that are commonly used include the diﬀerence in the treatment-speciﬁc sample averages of posttest response, namely the twosample comparison n

βˆn =

n

Ai Y2i

i=1 n i=1

− Ai

(1 − Ai )Y2i

i=1 n

,

(5.48)

(1 − Ai )

i=1

or possibly the diﬀerence in the treatment-speciﬁc sample averages of posttest minus pretest response, namely the two-sample change-score comparison Ai (Y2i − Y1i ) (1 − Ai )(Y2i − Y1i ) ˆ βn = . (5.49) − Ai (1 − Ai ) An analysis-of-covariance model has also been proposed for such designs, where it is assumed that Y2i = η0 + βAi + η1 Y1i + εi and β, η0 , and η1 are estimated using least squares. It is clear that the estimators βˆn given by (5.48) and (5.49) are semiparametric estimators for β in the sense that these are consistent and asymptotically normal estimators for β without having to make any additional parametric assumptions. It also turns out that the least-squares estimator for β in the analysis-of-covariance model is semiparametric, although this is not as obvious. A study of the relative eﬃciency of these estimators was made in Yang and Tsiatis (2001). In this paper, the parameter for treatment diﬀerence β in the pretest-posttest model was also cast using a simple restricted moment model for which the eﬃcient generalized estimating equation (GEE) could be derived. This GEE estimator was compared with the other more commonly used estimators for treatment diﬀerence in the pretest-posttest design. Rather than considering ad hoc semiparametric estimators for β, we now propose to look at this problem using the semiparametric theory that has been developed in this book. Toward that end, we will show how to derive the space of inﬂuence functions of RAL estimators for β. We will use the linear variety ϕ(Z) + T ⊥ to represent the space of inﬂuence functions. We begin by ﬁnding the inﬂuence function of some RAL estimator for β. For simplicity, we consider the inﬂuence function of βˆn (two-sample diﬀerence) from (5.48). This can be derived as Ai Y2i (1 − Ai )Y2i (1) (0) 1/2 ˆ 1/2 1/2 − µ2 −n , − µ2 n (βn − β0 ) = n Ai (1 − Ai )

5.4 Estimating Treatment Diﬀerence in a Randomized Pretest-Posttest Study (1)

129

(0)

where β0 = E(Y2 |A = 1) − E(Y2 |A = 0) = µ2 − µ2 . After some simple algebra, we obtain " " (1) (0) A (1 − A (Y − µ ) )(Y − µ ) i 2i i 2i 2 2 − n−1/2 n−1/2 ( Ai /n) ( (1 − Ai )/n) ⇓p ⇓p δ 1−δ Ai (1 − Ai ) (1) (0) −1/2 =n (Y2i − µ2 ) − (Y2i − µ2 ) + op (1). (5.50) δ (1 − δ) Consequently, the inﬂuence function for the i-th observation of βˆn equals ϕ(Zi ) =

Ai (1 − Ai ) (1) (0) (Y2i − µ2 ) − (Y2i − µ2 ). δ (1 − δ)

(5.51)

Now that we have identiﬁed one inﬂuence function for an RAL estimator for β, we can identify the linear variety of the space of inﬂuence functions by deriving the tangent space T and its orthogonal complement T ⊥ . The Tangent Space and Its Orthogonal Complement Let us now construct the tangent space T . The data from a single observation in a randomized pretest-posttest study are given by (Y1 , A, Y2 ). The only restriction that is placed on the density of the data is that induced by the randomization itself, speciﬁcally that the pretest measurement Y1 is independent of the treatment indicator A and that the distribution of the Bernoulli variable A is given by P (A = 1) = δ and P (A = 0) = 1 − δ, where δ is the randomization probability, which is known by design. Other than these restrictions, we will allow the density of (Y1 , A, Y2 ) to be arbitrary. To derive the tangent space and its orthogonal complement, we will take advantage of the results of partitioning the Hilbert space for a nonparametric model given by Theorem 4.5 of Chapter 4. First note that the density of the data for a single observation can be factored as pY1 ,A,Y2 (y1 , a, y2 ) = pY1 (y1 )pA|Y1 (a|y1 )pY2 |Y1 ,A (y2 |y1 , a).

(5.52)

With no restrictions on the distribution of Y1 , A, Y2 (i.e., the nonparametric model), we can use the results from Theorem 4.5 to show that the entire Hilbert space can be written as H = T1 ⊕ T2 ⊕ T3 , where

(5.53)

130

5 Other Examples of Semiparametric Models

T1 = {α1 (Y1 ) : E{α1 (Y1 )} = 0}, T2 = {α2 (Y1 , A) : E{α2 (Y1 , A)|Y1 } = 0}, T3 = {α2 (Y1 , A, Y2 ) : E{α2 (Y1 , A, Y2 )|Y1 , A} = 0}, and T1 , T2 , T3 are mutually orthogonal linear spaces. As mentioned above, in the semiparametric randomized pretest-posttest design, the only restrictions placed on the class of densities for Y1 , A, Y2 are those induced by randomization itself. Speciﬁcally, because of the independence of Y1 and A and the known distribution of A, we obtain pA|Y1 (a|y1 ) = PA (a) = δ a (1 − δ)(1−a) ; that is, the conditional density is completely known to us and not a function of unknown parameters. Otherwise, the density of Y1 , which is pY1 (y1 ), and the conditional density of Y2 given Y1 , A; i.e., pY2 |Y1 ,A (y2 |y1 , a), are arbitrary. Consequently, the tangent space for semiparametric models in the randomized pretest-posttest design is given by T = T1 ⊕ T3 . Remark 2. The contribution to the tangent space that is associated with the nuisance parameters for pA|Y1 (a|y1 ), which is T2 , is left out because, by the model restriction, this conditional density is completely known to us by design. The orthogonality of T1 , T2 , T3 together with (5.53) implies that the space orthogonal to the tangent space is given by T ⊥ = T2 . Using (4.22) from Theorem 4.5, we can represent elements of T2 as h∗2 (Y1 , A)− E{h∗2 (Y1 , A)|Y1 } for any arbitrary function h∗2 (·) of Y1 and A. Because A is a binary indicator function, any function h∗2 (·) of Y1 and A can be expressed as h∗2 (Y1 , A) = Ah1 (Y1 )+h2 (Y1 ), where h1 (·) and h2 (·) are arbitrary function of Y1 . Therefore h∗ (Y1 , A) − E{h∗ (Y1 , A)|Y1 } = Ah1 (Y1 ) + h2 (Y1 ) − {E(A|Y1 )h1 (Y1 ) + h2 (Y1 )} = (A − δ)h1 (Y1 ). Consequently, we have shown that the orthogonal complement of the tangent space is all elements {(A − δ)h∗ (Y1 )} for any arbitrary function h∗ (·) of Y1 and therefore the space of all inﬂuence functions, {ϕ(Z) + T ⊥ }, is A (1 − A) (1) (0) (5.54) (Y2 − µ2 ) − (Y2 − µ2 ) + (A − δ)h∗ (Y1 ) δ (1 − δ) for any arbitrary function h∗ (Y1 ).

5.4 Estimating Treatment Diﬀerence in a Randomized Pretest-Posttest Study

131

An estimator for β with this inﬂuence function is given by / 0 Ai Y2i (1 − Ai )Y2i + n−1 Ai − n−1 Ai h∗ (Y1i ). − Ai (1 − Ai ) The estimator above will be a consistent, asymptotically normal semiparametric estimator for β. Moreover, the class of estimators above, indexed by the functions h∗ (Y1 ), are RAL estimators with inﬂuence functions that include the entire class of inﬂuence functions. Although all the estimators given above are asymptotically normal, the asymptotic variance will vary according to the choice of h∗ (·). We showed in Theorem 4.3 that the eﬃcient inﬂuence function is given by ϕ(Z) − Π{ϕ (Z)|T ⊥ }, or for our problem A A (1) (1) (Y2 − µ2 ) − Π (Y2 − µ2 )|T ⊥ δ δ 1−A 1−A (0) (0) − (Y2 − µ2 ) + Π (Y2 − µ2 )|T ⊥ . 1−δ 1−δ

(5.55)

Since T ⊥ = T2 , we can use (4.23) of Theorem 4.5 to show that for any function α(Y1 , A, Y2 ), Π{α(·)|T ⊥ } = Π{α(·)|T2 } = E{α(·)|Y1 , A} − E{α(·)|Y1 }. Therefore A A A (1) (1) (1) ⊥ (Y2 − µ2 )|T (Y2 − µ2 )|Y1 , A − E (Y2 − µ2 )|Y1 , Π =E δ δ δ (5.56) where A A (1) (1) E E(Y2 |Y1 , A = 1) − µ2 , (5.57) (Y2 − µ2 )|Y1 , A = δ δ (1) and by the law of iterated conditional expectations, E Aδ (Y2 − µ2 )|Y1 can be computed by taking the conditional expectation of (5.57) given Y1 , yielding A (1) (1) (Y2 − µ2 )|Y1 = E(Y2 |Y1 , A = 1) − µ2 . E (5.58) δ Therefore, by (5.56)–(5.58), we obtain A A−δ (1) (1) E(Y2 |Y1 , A = 1) − µ2 Π . (Y2 − µ2 )|T ⊥ = δ δ

(5.59)

132

5 Other Examples of Semiparametric Models

Similarly, we obtain 1−A A−δ (0) (0) ⊥ Π (Y2 − µ2 )|T E(Y2 |Y1 , A = 0) − µ2 =− . 1−δ 1−δ

(5.60)

Using (5.59) and (5.60) and after some algebra, we deduce that the eﬃcient inﬂuence function (5.55) equals A (A − δ) Y2 − E(Y2 |A = 1, Y1 ) δ δ (1 − A) (A − δ) Y2 + E(Y2 |A = 0, Y1 ) − (1 − δ) (1 − δ) / 0 (1) (0) − µ2 − µ2 . (5.61)

β In order to construct an eﬃcient RAL estimator for β (that is, an RAL estimator for β whose inﬂuence function is the eﬃcient inﬂuence function given by (5.61)), we would need to know E(Y2 |A = 1, Y1 ) and E(Y2 |A = 0, Y1 ), which, of course, we don’t. One strategy is to posit models for E(Y2 |A = 1, Y1 ) and E(Y2 |A = 0, Y1 ) in terms of a ﬁnite number of parameters ξ1 and ξ0 , respectively. That is, E(Y2 |A = j, Y1 ) = ζj (Y1 , ξj ), j = 0, 1. These posited models are restricted moment models for the subset of individuals in treatment groups A = 1 and A = 0, respectively. For such models, we can use generalized estimating equations to obtain estimators ξˆ1n and ξˆ0n for ξ1 and ξ0 using patients {i : A1 = 1} and {i : Ai = 0}, respectively. Such estimators are consistent for ξ1 and ξ0 if the posited models were correctly speciﬁed, but, even if they were incorrectly speciﬁed, under suitable regularity conditions, ξˆjn would converge to ξj∗ for j = 0, 1. With this in mind, we use the functional form of the eﬃcient inﬂuence given by (5.61) to motivate the estimator βˆn for β given by n Ai (Ai − δ) −1 ˆ ˆ Y2i − ζ1 (Y1i , ξ1n ) βn = n δ δ i=1 (Ai − δ) (1 − Ai ) Y2i + ζ0 (Y1i , ξˆ0n ) . (5.62) − (1 − δ) (1 − δ) After some algebra (Exercise 3 at the end of this Chapter), we can show that the inﬂuence function of βˆn is A (A − δ) (1) (1) (Y2 − µ2 ) − {ζ1 (Y1 , ξ1∗ ) − µ2 } δ δ (A − δ) (1 − A) (0) (0) − (Y2 − µ2 ) − {ζ0 (Y1 , ξ0∗ ) − µ2 }. (1 − δ) (1 − δ)

(5.63)

5.5 Remarks about Auxiliary Variables

133

This inﬂuence function is in the class of inﬂuence functions of RAL estimators for β given by (5.54) regardless of whether we posited the correct model for E(Y2 |Y1 , A = 1) and E(Y2 |Y1 , A = 0) or not. Consequently, the estimator βˆn given by (5.62) is locally eﬃcient in the sense that this estimator leads to a consistent asymptotically normal semiparametric RAL estimator for β even if the posited models for the treatment-speciﬁc conditional expectations of Y2 given Y1 are incorrect, but this estimator is semiparametric eﬃcient if the posited model is correct. Our experience working with such adaptive methods is that a reasonable attempt at modeling the conditional expectations using the usual modelbuilding techniques that statisticians learn will lead to estimators with very high eﬃciency even if the model is not exactly correct. For more details, see Leon, Tsiatis, and Davidian (2003). Although the development above was speciﬁc to the pretest-posttest study, this can be easily generalized to the more general problem of covariate adjustment in a two-arm randomized study. Letting Y2 be the response variable of interest and A the treatment indicator, assigned at random to patients, the primary goal of a two-arm randomized study is to estimate the parameter β = E(Y2 |A = 1) − E(Y2 |A = 0). Let Y1 be a vector of baseline covariates that are also collected on all individuals prior to randomization. One of the elements of Y1 could be the same measurement that is used to evaluate response, as was the case in the pretest-posttest study. In such a study, the data are realizations of the iid random vectors (Y1i , Ai , Y2i ), i = 1, . . . , n. As before, in a semiparametric model, the only restriction on the class of densities for (Y1 , A, Y2 ) is that induced by randomization itself; namely, that A is independent of Y1 and that the P (A = 1) = δ. Consequently, the factorization of the data (Y1 , A, Y2 ) is identical to that given by (5.52) and hence the eﬃcient inﬂuence function is given by (5.61), with the only diﬀerence being that Y1 now represents the vector of baseline covariates rather than the single pretest measurement made at baseline in the pretest-posttest study. In fact, the exact same strategy for obtaining locally eﬃcient adaptive semiparametric estimators for β as given by (5.62) for the pretest-posttest study can be used for this more general problem.

5.5 Remarks about Auxiliary Variables In the randomized pretest-posttest problem presented in the previous section, the parameter of interest, β = E(Y2 |A = 1) − E(Y2 |A = 0), is obtained from the joint distribution of (Y2 , A). Nonetheless, we were able to use the random variable Y1 , either the pretest measurement in a pretest-posttest study or a vector of baseline characteristics, to obtain a more eﬃcient estimator of the treatment diﬀerence β. One can view the variables making up Y1 as auxiliary variables, as they are not needed to deﬁne the parameter of interest.

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5 Other Examples of Semiparametric Models

Therefore, a natural question, when we are considering estimating the parameter β in a model p(z, β, η) for the random vector Z, is whether we can gain eﬃciency by collecting additional auxiliary variables W and deriving estimators for β using the data from both Z and W . We will now argue that if we put no additional restrictions on the distribution of (W, Z) other than those from the marginal model for Z, then the class of RAL estimators is the same as if we never considered W . To see this, let us ﬁrst make a distinction between the Hilbert space HZ (i.e., all q-dimensional mean-zero square-integrable functions of Z) and HW Z (i.e., all q-dimensional mean-zero square-integrable functions of W and Z). If we consider estimators for β based only on the data from Z, then we have developed a theory that shows that the class of inﬂuence functions of RAL estimators for β can be represented as ⊥

ϕ(Z) + T Z ,

(5.64) ⊥

where ϕ(Z) is the inﬂuence function of any RAL estimator for β and T Z is the space orthogonal to the tangent space deﬁned in HZ . However, if, in addition, we consider auxiliary variables W , then the space of inﬂuence functions of RAL estimators for β is given by ⊥

ϕ(Z) + T W Z ,

(5.65)

⊥

where T W Z is the space orthogonal to the tangent space deﬁned in HW Z . We now present the key result that demonstrates that, without additional assumptions on the conditional distribution of auxiliary covariates W given Z, the class of inﬂuence functions of RAL estimators for β remains the same. Theorem 5.6. If no restrictions are put on the conditional density pW |Z (w|z), where the marginal density of Z is assumed to be from the semiparametric model pZ (z, β, η), then the orthogonal complement of the tangent space T W Z ⊥ for the semiparametric model for the joint distribution of (W, Z) (i.e., T W Z ) is equal to the orthogonal complement of the tangent space T Z for the semi⊥ parametric model of the marginal distribution for Z alone (i.e., T Z ). Proof. With no additional restrictions placed on the joint distribution of (W, Z), the model can be represent by the class of densities pW,Z (w, z, β, η, η ∗ ) = pW |Z (w|z, η ∗ )pZ (z, β, η),

(5.66)

density of W given Z; where pW |Z (w|z, η ∗ ) can be any arbitrary conditional i.e., any positive function η ∗ (w, z) such that η ∗ (w, z)dνW (w) = 1 for all z. In Theorem 4.5, we showed that the part of the tangent space associated with the inﬁnite-dimensional parameter η ∗ was the set of random functions q q q×1 T1 = a (W, Z) : E{a (W, Z)|Z} = 0

5.6 Exercises for Chapter 5

135

and, moreover, that the entire Hilbert space HW Z can be partitioned as HW Z = T1 ⊕ HZ , T1 ⊥ HZ . Because the density in (5.66) factors into terms involving the parameter η ∗ and terms involving the parameters (β, η), where η ∗ and (β, η) are variationally independent, it is then straightforward to show that the tangent space for the model on (W, Z) is given by T W Z = T1 ⊕ T Z , T1 ⊥ T Z . It is also straightforward to show that the space orthogonal to the tangent space T W Z is the space that is orthogonal both to T1 and to T Z ; i.e., it must be the space within HZ that is orthogonal to T Z . In other words, the ⊥ ⊥ space T W Z is the same as the space T Z . ⊥

⊥

Because T W Z = T Z , this means that the space of inﬂuence functions of RAL estimators for β given by (5.64), using estimators that are functions of Z alone, is identical to the space of inﬂuence functions of RAL estimators for β given by (5.65), using estimators that are functions of W and Z. This formally validates the intuitive notion that if we are not willing to make any additional assumptions regarding the relationship of the auxiliary variables W and the variables of interest Z, then we need not consider auxiliary variables when estimating β. The reason we gained eﬃciency in the pretest-posttest problem was because a relationship was induced between Y1 and the treatment indicator A due to randomization; namely, that Y1 was independent of A.

5.6 Exercises for Chapter 5 1. Heteroscedastic models Consider the semiparametric model for which, for a one-dimensional response variable Y , we assume Y = µ(X, β) + V 1/2 (X, β)ε, β ∈ Rq , where ε is an arbitrary continuous random variable such that ε is independent of X. To avoid identiﬁability problems, assume that for any scalars α, α α + µ(x, β) = α + µ(x, β )

for all x

implies α = α

and β = β ,

136

5 Other Examples of Semiparametric Models

and for any scalars σ, σ > 0 that σ{V (x, β)} = σ {V (x, β )}

for all x

implies σ = σ

and β = β .

For this model, describe how you would derive a locally eﬃcient estimator for β from a sample of data (Yi , Xi ), i = 1, . . . , n. 2. In the pretest-posttest study, one estimator for the treatment diﬀerence β that has been proposed is the analysis of covariance (ANCOVA) estimator. That is, an analysis-of-covariance model is assumed, where Y2i = η0 + βAi + η1 Y1i + εi

(5.67)

for the iid data (Y1i , Ai , Y2i ), i = 1, . . . , n, and the parameters η0 , β, η1 are estimated using least squares. a) Show that the least-squares estimator βˆn in the model above is a semiparametric estimator for β = E(Y2 |A = 1) − E(Y2 |A = 0); that is, that n1/2 (βˆn − β) converges to a normal distribution with mean zero whether the linear model (5.67) is correct or not. b) Find the inﬂuence function for βˆn and show that it is in the class of inﬂuence functions given in (5.54). 3. In (5.62) we considered locally eﬃcient adaptive estimators for the treatment diﬀerence β in a randomized pretest-posttest study. Suppose the estimators ξˆjn , j = 0, 1 were root-n consistent estimators; that is, that there exist ξj∗ , j = 0, 1 such that n1/2 (ξˆjn − ξj∗ ) are bounded in probability for j = 0, 1 and that the functions ζj (x1 , ξj ), j = 0, 1 as functions in ξj were diﬀerentiable in a neighborhood of ξj∗ , j = 0, 1 for all x1 . Then show (heuristically) that the inﬂuence function for the estimator βˆn given in (5.62) is that given in (5.63).

6 Models and Methods for Missing Data

6.1 Introduction In many practical situations, although we may set out in advance to collect data according to some “nice” plan, things may not work out quite as intended. Some examples of this follow. Nonresponse in sample surveys We send out questionnaires to a sample of (randomly chosen) individuals. However, some may provide only a partial answer or no answer to some questions, or, perhaps, may not return the questionnaire at all. Dropout or noncompliance in clinical trials A study is conducted to compare two or more treatments. In a randomized clinical trial, subjects are enrolled into the study and then randomly assigned to one of the treatments. Suppose, in such a clinical trial, the subjects are supposed to return to the clinic weekly to provide response measurement Yij (for subject i, week j). However, some subjects “drop out” of the study, failing to show up for any clinic visit after a certain point. Still others may miss clinic visits sporadically or quit taking their assigned treatment. Surrogate measurements For some studies, the response of interest or some important covariate may be very expensive to obtain. For example, suppose we are interested in the daily average percentage fat intake of a subject. An accurate measurement requires a detailed “food diary” over a long period, which is both expensive and time consuming. A cheaper measurement (surrogate) is to have subjects recall the food they ate in the past 24 hours. Clearly, this cheaper measurement will be correlated with the expensive one but not perfectly. To reduce costs, a study may be conducted where only a subsample of participants provide the expensive measurement (validation sample), whereas everyone provides data on the inexpensive measurement. The expensive measurement would be

138

6 Models and Methods for Missing Data

missing for all individuals not in the validation sample. Unlike the previous examples, here the missingness was by design rather than by happenstance. In almost all studies involving human subjects, some important data may be missing for some subjects for a variety of reasons, from oversight or mistakes by the study personnel to refusal or inability of the subjects to provide information. Objective: Usually, interest focuses on making an inference about some aspect (parameter) of the distribution of the “full data” (i.e., the data that would have been observed if no data were missing). Problem: When some of the data are missing, it may be that, depending on how and why they are missing, our ability to make an accurate inference may be compromised. Example 1. Consider the following (somewhat contrived) problem. A study is conducted to assess the eﬃcacy of a new drug in reducing blood pressure for patients that have hypertension using a randomized design, where half of the patients recruited with hypertension are randomized to receive the new treatment and the other half are given a placebo. The endpoint of interest is the decrease in blood pressure after six months. Let µ1 denote the mean decrease in blood pressure (after six months) if all patients in a population were given the new treatment and µ0 the population mean decrease if given a placebo. The parameter of interest is the mean treatment diﬀerence, δ = µ1 − µ0 . If all the patients randomized into this study are followed for six months and have complete measurements, then an unbiased estimator for δ can be computed easily using the diﬀerence in sample averages of blood pressure decrease from the patients in the two treatment groups. Suppose, however, that some of the data are missing. Consider the scenario where all the data for patients randomized to the placebo were collected, but some of the patients assigned treatment dropped out and hence their six-month blood pressure reading is missing. For such a problem, what would we do? This problem can be deﬁned using the following notation. For individual i in our sample i = 1, · · · , n, let ⎧ ⎨1 denotes treatment assignment, Ai = ⎩0 and Yi = reduction in blood pressure after six months. If we had “full” data (i.e., if we observed {Ai , Yi }, i = 1, · · · , n), then

6.1 Introduction n

µ ˆ1 =

n

Ai Yi

i=1 n

,µ ˆ0 = Ai

i=1

139

(1 − Ai )Yi

i=1 n

, (1 − Ai )

i=1

would be unbiased estimators for µ1 and µ0 , respectively. Let Ri denote the indicator of complete data for the i-th individual; i.e., 1 if the six-month blood pressure measurement was taken Ri = 0 if it was missing. In such a case, we would only observe (i.e., observed data) (Ai , Ri , Ri Yi ), i = 1, · · · , n; that is, we always observe Ai and Ri but only observe Yi if Ri = 1. It is important to emphasize the distinction between full data, observed data, and complete data, as we will be using this terminology throughout the remainder of the book. Full data are the data that we would want to have collected on all the individuals in the sample i = 1, . . . , n. Observed data are the data that are actually observed on the individuals in the study, some of which are missing. Complete data are the data from only the subset of patients with no missing data. In this hypothetical scenario, data may be missing only if Ai = 1. Therefore, among patients randomized to the placebo, there are no missing data and therefore we can consistently (unbiasedly) estimate µ0 using the treatmentspeciﬁc sample average n (1 − Ai )Yi µ ˆ0 =

i=1 n

. (1 − Ai )

i=1

Let us therefore focus our attention on estimating µ1 . Since we will only be considering this one sample for the time being, we will use the notation (Ri , Y1i ), i = 1, · · · , n1 to represent the full data for the n1 individuals randomized to treatment 1, n n1 = Ai , and focus on this subset of data. The observed data are i=1

(Ri , Ri Y1i ), i = 1, · · · , n1 . A natural estimator for µ1 , using the observed data, is the complete-case sample average; namely, n1 Ri Y1i µ ˆ1c =

i=1 n1 i=1

. Ri

140

6 Models and Methods for Missing Data

We now examine whether this estimator is reasonable or not under various circumstances. Intuitively, if we believed that missing observations occurred by chance alone, then we might expect that the complete cases will still be representative of the population from which they were sampled and consequently would still give us an unbiased estimator. If, however, there was a systematic bias, where, say, patients with a worse prognosis (i.e., lower values of Y ) were more likely to drop out, then we might expect the complete-case estimator to be overly optimistic. This can be formalized as follows. Let the full data be denoted by (Ri , Y1i ), i = 1, . . . , n1 , assumed to be iid with E(Y1i ) = µ1 . Note 1. In actuality, we cannot observe Y1i whenever Ri = 0; nonetheless, this conceptualization is useful in understanding the consequences of missingness. The joint density of (Ri , Y1i ) can be characterized by pR,Y1 (ri , y1i ) = pR| Y1 (ri |y1i ) pY1 (y1i ). Since Ri is binary, we only need P (Ri = 1|Y1i = y1i ) = π(y1i ) to specify the conditional density because pR|Y1 (ri |y1i ) = {π(y1i )}ri {1 − π(y1i )}1−ri . Again, the observed data are denoted by (Ri , Ri Y1i ), i = 1, . . . , n1 . If the data are missing completely at random, denoted as MCAR (i.e., P (R = 1|Y1 ) = π(Y1 ) = π), then the probability of being observed (or missing) does not depend on Y1i . That is, R and Y1 are independent; R ⊥ ⊥ Y1 . The complete-case estimator n1

n−1 1

Ri Y1i

i=1 n1 i=1

= Ri

n1

Ri Y1i P i=1 → n1 n−1 Ri 1 i=1

E(RY1 ) , E(R)

and by the independence of R and Y1 , E(R)E(Y1 ) E(RY1 ) = = E(Y1 ) = µ1 . E(R) E(R)

6.1 Introduction

141

Therefore, if the data are missing completely at random (MCAR), then the complete-case estimator is unbiased, as our intuition would suggest. If, however, the probability of missingness depends on Y1 , which we will refer to as nonmissing at random, (NMAR) (a formal deﬁnition will be given in later chapters), then the complete-case estimator is written n1

Ri Y1i

i=1 n1

P

− → Ri

E{E(RY1 )|Y1 } E(RY1 ) = E(R) E{E(R|Y1 )}

(6.1)

i=1

=

E{Y1 π(Y1 )} = E(Y1 ) E{π(Y1 )}

(necessarily).

In fact, if π(y) is an increasing function in y (i.e., probability of not being missing increases with y), then this suggests that individuals with larger values of Y would be overrepresented in the observed data and hence E{Y1 π(Y1 )} > µ1 . E{π(Y1 )} We leave the proof of this last result as an exercise for the reader. The diﬃculty with NMAR is that there is no way of estimating π(y) = P (R = 1|Y1 = y) from the observed data because if R = 0 we don’t get to observe Y1 . In fact, there is no way that we can distinguish whether the missing data were MCAR or NMAR from the observed data. That is, there is an inherent nonidentiﬁability problem here. There is, however, a third possibility to consider. Suppose, in addition to the response variable, we measured additional covariates on the i-th individual, denoted by Wi , which are not missing. For example, some baseline characteristics may also be collected, including baseline blood pressure or possibly some additional variables collected between the initiation of treatment and six months, when the follow-up response is supposed to get collected. Such variables Wi , i = 1, . . . , n are sometimes referred to as auxiliary covariates, as they represent variables that are not of primary interest for inference. The observable data in such a case are denoted by (Ri , Ri Y1i , Wi ),

i = 1, · · · , n1 .

Although the auxiliary covariates are not of primary interest, suppose we believe that the reason for missingness depends on Wi , and, moreover, conditional on Wi , Y1i has no additional eﬀect on the probability of missingness. Speciﬁcally, P (Ri = 1 Y1i , Wi ) = π(Wi ). (6.2) That is, conditional on Wi , Y1i is independent of Ri ; Ri ⊥ ⊥ Y1i Wi .

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6 Models and Methods for Missing Data

Remark 1. It may be that Wi is related to both Y1i and Ri , in which case, even though (6.2) is true, a dependence between Y1i and Ri would be induced. For example, consider the hypothetical scenario where Wi denotes the blood pressure for the i-th individual at an interim examination, say at three months, measured on all n individuals in the sample. After observing this response, individuals whose blood pressure was still elevated would be more likely to drop out. Therefore, Ri would be correlated with Wi . Since individuals could not possibly know what their blood pressure is at the end of the study (i.e., at six months), it may be reasonable to assume that Ri ⊥ ⊥ Y1i |Wi . It may also be reasonable to assume that the three-month blood pressure reading is correlated with the six-month blood pressure reading. Under these circumstances, dropping out of the study Ri would be correlated with the response outcome Y1i but in this case, because they were both related to the interim three-month blood pressure reading. Therefore, without conditioning additionally on Wi , we would obtain that P (Ri = 1 Y1i ) = π(Y1i ) depends on the value Y1i . Consequently, if we didn’t collect the additional data Wi , then we would be back in the impossible NMAR situation. The assumption (6.2) is an example of what is referred to as missing at random, or MAR (not to be confused with MCAR). Basically, missing at random means that the probability of missingness depends on variables that are observed. A general and more precise deﬁnition of MAR will be given later. The MAR assumption alleviates the identiﬁability problems that were encountered with NMAR because the probability of missingness depends on variables that are observed on all subjects. The available data could also be used to model the relationship for the probability of missingness, or, equivalently, the probability of a complete case, as a function of the covariates Wi . For example, we can posit a model for P (R = 1|W = w) = π(w, γ) (say, logistic regression) in terms of a parameter vector γ and estimate the parameter γ from the observed data (Ri , Wi ), which are measured on all individuals i = 1, . . . , n, using, say, maximum likelihood. That is, the maximum likelihood estimator γˆ would be obtained by maximizing n !

π(Wi , γ)Ri {1 − π(Wi , γ)}1−Ri .

i=1

This probability of a complete case as a function of the covariates, together with the MAR assumption, will prove useful for computing inverse probability weighted complete-case estimators, to be described later. There are several methods for estimating parameters when data are missing at random. These methods include:

6.2 Likelihood Methods

143

(a) likelihood methods; (b) imputation methods; (c) inverse probability weighting of complete cases. Likelihood and imputation methods have been studied in great detail, and some excellent references include the books by Rubin (1987), Little and Rubin (1987), and Schafer (1997). The main theory of inverse probability weighted methods is given in the seminal paper by Robins, Rotnitzky, and Zhao (1994) and will be the primary focus of this book. To give a ﬂavor of how these methods work, let us return to the problem of estimating µ1 = E(Y1 ) when auxiliary variables W are collected and the response data are missing at random; i.e., R⊥ ⊥ Y1 |W.

6.2 Likelihood Methods Consider a parametric model with density pY1 ,W (y1 , w) = pY1 |W (y1 |w, γ1 ) pW (w, γ2 ),

(6.3)

where γ1 , γ2 are unknown parameters describing the conditional distribution of Y1 given W and the marginal distribution of W , respectively. The parameter µ1 we are interested in can be written as µ1 = E(Y1 ) = E{E(Y1 |W )} = ypY1 |W (y|w, γ1 )pW (w, γ2 )dνY (y)dνW (w). If we could obtain estimators for γ1 , γ2 , say γˆ1 , γˆ2 , respectively, then we could estimate µ1 , by (6.4) µ ˆ1 = ypY1 |W (y|w, γˆ1 )pW (w, γˆ2 )dνY (y)dνW (w). A popular way of obtaining estimators is by maximizing the likelihood. The density of the observed data for one individual can be written as I(r=1)

pRY1 ,W,R (ry1 , w, r) = {pY1 ,W,R (y1 , w, r = 1)} or '

(I(r=1) pY1 |W,R (y1 |w, r = 1) pR|W (r = 1|w)pW (w) ' (I(r=0) pR|W (r = 0|w) pW (w) .

Because of MAR (i.e., R ⊥ ⊥ Y1 |W ),

I(r=0)

{pW,R (w, r = 0)}

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6 Models and Methods for Missing Data

pY1 |W,R (y1 |w, r = 1) = pY1 |W (y1 |w). Therefore, the likelihood for one individual in our sample is given by '

(I(r=1) pY1 |W (y1 |w, γ1 ) pW (w, γ2 ) × {π(w, γ3 )}I(r=1) {1 − π(w, γ3 )}I(r=0) ,

where pR|W (r = 1|w) = π(w, γ3 ). Consequently, the likelihood for n independent sets of data is given as n " n " ! ! I(ri =1) pY1 |W (y1i |wi , γ1 ) pW (wi , γ2 ) × {function of γ3 }. i=1

i=1

Because of the way the likelihood factorizes, we ﬁnd the MLE for γ1 , γ2 by separately maximizing ! pY1 |W (y1i |wi , γ1 ) (6.5) {i : Ri =1}

and

n !

pW (wi , γ2 ).

(6.6)

i=1

Note 2. We only include complete cases to ﬁnd the MLE for γ1 , whereas we use all the data to ﬁnd the MLE for γ2 . The estimates for γ1 and γ2 , found by maximizing (6.5) and (6.6), can then be substituted into (6.4) to obtain the MLE for µ1 . Remark 2. Although likelihood methods are certainly feasible and the corresponding estimators enjoy the optimality properties aﬀorded to an MLE, they can be diﬃcult to compute in some cases. For example, the integral given in (6.4) can be numerically challenging to compute, especially if W involves many covariates.

6.3 Imputation Since some of the Y1i ’s are missing, a natural strategy is to impute or “estimate” a value for such missing data and then estimate the parameter of interest behaving as if the imputed values were the true values. For example, if there were no missing values of Y1i , i = 1, . . . , n1 , then we would estimate µ1 by using

6.3 Imputation

µ ˆ1 =

n−1 1

n1

Y1i .

145

(6.7)

i=1

However, for values of i such that Ri = 0, we don’t observe such Y1i . Suppose we posit some relationship for the distribution of Y1 given W ; e.g., we may assume a parametric model pY1 |W (y, |w, γ1 ) as we did in (6.3). By the MAR assumption, we can estimate γ1 using the complete cases, say, by maximizing (6.5) to derive the MLE γˆ1 . This would allow us to estimate ypY1 |W (y|w, γˆ1 )dνY (y), E(Y1 W = w) by which we denote by µ(w, γˆ1 ). We then propose to impute the missing data for any individual i such that Ri = 0 by substituting the value µ(Wi , γˆ1 ) for the missing Yi in (6.7). The resulting imputed estimator is n−1 1

n1

{Ri Y1i + (1 − Ri )µ(Wi , γˆ1 )} .

(6.8)

i=1

A heuristic argument why this should yield a consistent estimator is as follows. Assuming γˆ1 converges to the truth, then (6.8) should be well approximated by n1 −1 {Ri Y1i + (1 − Ri )µ(Wi , γ10 )} + op (1), (6.9) n1 i=1

where op (1) is a term converging in probability to zero and, at the truth, γ10 , µ(Wi , γ10 ) = E(Y1i Wi ). Therefore, by the weak law of large numbers (WLLN), (6.9) converges in probability to E{RY1 + (1 − R)E(Y1 |W )}. By a conditioning argument, this equals E [E{RY1 + (1 − R)E(Y1 |W )|R, W }] = E{R E(Y1 |R, W ) + (1 − R)E(Y1 |W )} * +, by MAR = E(Y1 |W ) = E{RE(Y1 |W ) + (1 − R)E(Y1 |W )} = E{E(Y1 |W )} = E(Y1 ) = µ1 . Remarks 1. We could have modeled the conditional expectation directly, say as E(Y1 |W ) = µ(W, γ), and estimated γ by using GEEs with complete cases.

146

6 Models and Methods for Missing Data

2. Later, we will consider other imputation techniques, where we impute a missing Y1i by using a random draw from the conditional distribution of pY1 |Wi (y1 |Wi , γˆ1 ) or possibly using more than one draw (multiple imputation).

6.4 Inverse Probability Weighted Complete-Case Estimator When data are MAR (i.e., Y1 ⊥ ⊥ R|W ), we have already argued that Y1 may not be independent of R. Therefore, the naive complete-case estimator (6.1) n1

Ri Y1i

i=1 n1

Ri

i=1

may be biased. Horvitz and Thompson (1952), and later Robins, Rotnitzky, and Zhao (1994), suggested using inverse weighting of complete cases as a method of estimation. Let us denote the probability of observing a complete case by P (R = 1|W, Y1 ) = P (R = 1|W ) = π(W ). * +, ⇓ follows by MAR The basic intuition is as follows. For any individual randomly chosen from a population with covariate value W , the probability that such an individual will have complete data is π(W ). Therefore, any individual with covariate 1 W with complete data can be thought of as representing π(W ) individuals at random from the population, some of which may have missing data. This suggests using n1 Ri Y1i µ ˆ1 = n−1 1 π(W i) i=1 P RY1 as an estimator for µ1 . By WLLN, µ ˆ1 − → E π(W ) , which, by conditioning, equals Y1 RY1 Y1 , W =E E(R|Y1 , W ) E E π(W ) π(W ) Y1 π(W ) = E(Y1 ) = µ1 . =E π(W )

6.5 Double Robust Estimator

147

In many practical situations, π(W ) = P (R = 1|W ) would not be known to us. In such cases, we may posit a model P (R = 1|W = w) = π(w, γ3 ), and γ3 can be estimated by maximizing the likelihood n1 !

{π(Wi , γ3 )}Ri {1 − π(Wi γ3 )}1−Ri

i=1

to obtain the estimator γˆ3 . (Often, logistic regression models are used.) The resulting inverse probability weighted complete-case (IPWCC) estimator is given by n1 Ri Y1i . (6.10) n−1 1 π(Wi , γˆ3 ) i=1 Remark 3. There is a technical condition that π(w) be strictly greater than zero for all values of w in the support of W in order that the IPWCC estimator (6.10) be consistent and asymptotically normal. This will be discussed in greater detail in later chapters. A cautionary note, however, even if this technical condition holds true, is that if π(Wi , γˆ3 ) is very small, then this gives undue inﬂuence to the i-th observation in the IPWCC estimator (6.10). This could result in a very unstable estimator with poor performance with small to moderate sample sizes.

6.5 Double Robust Estimator For both the likelihood estimator and the imputation estimator, we had to specify a model for pY1 |W (y1 |w, γ1 ). If this model was incorrectly speciﬁed, then both of these estimators would be biased. For the IPWCC estimator, we had to specify a model for the probability of missingness P (R = 1|W = w) = π(w, γ3 ). If this model was incorrectly speciﬁed, then the IPWCC estimator would be biased. More recently, augmented inverse probability weighted complete-case estimators have been suggested that are doubly robust. Scharfstein, Rotnitzky, and Robins (1999) ﬁrst introduced the notion of double robust estimators. Double robust estimators were also studied by Lipsitz, Ibrahim, and Zhao (1999), Robins (1999), Robins, Rotnitzky, and van der Laan (2000), Lunceford and Davidian (2004), Neugebauer and van der Laan (2005), Robins and Rotnitzky (2001), and van der Laan and Robins (2003); the last two references give the theoretical justiﬁcation for these methods. An excellent overview is also given by Bang and Robins (2005). To obtain such an estimator, a model is speciﬁed for E(Y1 |W ) = µ(W, γ1 ) and for

(6.11)

148

6 Models and Methods for Missing Data

P (R = 1|W = w) = π(w, γ3 ),

(6.12)

where the parameters γ1 and γ3 can be estimated as we have already discussed. Denote these estimators by γˆ1 and γˆ3 . If (6.11) is correctly speciﬁed, then γˆ1 will converge in probability to γ10 (the truth), in which case P

µ(W, γˆ1 ) − → µ(W, γ10 ) = E(Y1 |W ), P

whereas if (6.11) is incorrectly speciﬁed, then γˆ1 − → γ1∗ and µ(W, γˆ1 ) − → µ(W, γ1∗ ) = E(Y1 |W ). P

Similarly, if (6.12) is correctly speciﬁed, then P

π(W, γˆ3 ) − → π(W, γ30 ) = P (R = 1|W ), and if incorrectly speciﬁed π(W, γˆ3 ) − → π(W, γ3∗ ) = P (R = 1|W ). P

Consider the estimator n1 {Ri − π(Wi , γˆ3 )} Ri Y1i − µ(W µ ˆ1 = n−1 , γ ˆ ) . i 1 1 π(Wi , γˆ3 ) π(Wi , γˆ3 ) i=1

(6.13)

This estimator is referred to as an augmented inverse probability weighted complete-case (AIPWCC) estimator. Notice that the ﬁrst term in (6.13) is the inverse probability weighted complete-case estimator derived in Section 6.4. This term only involves contributions from complete cases. The second term includes additional contributions from individuals with some missing data. This is the so-called augmented term. We will now show that this AIPWCC estimator is doubly robust in the sense that it is consistent if either the model (6.11) for Y1 given W is correctly speciﬁed (i.e., µ(W, γ10 ) = E(Y1 |W )) or the missingness model (6.12) is correctly speciﬁed (i.e., π(W, γ30 ) = P (R = 1|W )). The estimator (6.13) can be reexpressed as n−1 1

n1 {Ri − π(Wi , γˆ3 )} Y1i + {Y1i − µ(Wi , γˆ1 )} . π(Wi , γˆ3 ) i=1

(a) Suppose the model (6.12) is correctly speciﬁed but the model (6.11) might not be. Then the estimator is approximated by n1 {Ri − P (R = 1|Wi )} −1 ∗ {Y1i − µ(Wi , γ1 )} + op (1), Y1i + n1 P (R = 1|Wi ) i=1 which converges to

6.5 Double Robust Estimator

149

R − P (R = 1|W ) {Y1 − µ(W, γ1∗ )} E Y1 + P (R = 1|W ) R − P (R = 1|W ) {Y1 − µ(W, γ1∗ )} = E(Y1 ) + E P (R = 1|W ) * +, Conditioning on Y1 , W E[E[ |Y1 , W ]]

⎡ ⎤ E(R|Y , W ) − P (R = 1|W ) 1 E⎣ {Y1 − µ(W, γ1∗ )}⎦ P (R = 1|W ) * +,

P (R = 1|W ) − P (R = 1|W ) P (R = 1|W )

0 = E(Y1 ) = µ1 . (b) Suppose the model (6.11) is correctly speciﬁed but the model (6.12) might not be. Then the estimator is n1 Ri − π(Wi , γ3∗ ) −1 {Y1i − E(Y1 |Wi )} + op (1), n1 Y1i + π(Wi , γ3∗ ) i=1 which converges to R − π(W, γ3∗ ) {Y − E(Y |W )} E Y1 + 1 1 π(W, γ3∗ ) R − π(W, γ3∗ ) {Y = E(Y1 ) + E − E(Y |W )} 1 1 π(W, γ3∗ ) * +, -

=E

R − π(W, γ3∗ ) π(W, γ3∗ )

Condition on R, W E[E[ |R, W ]]

{E(Y1 |R, W ) − E(Y1 |W )}

{E(Y1 |W ) − E(Y1 |W )}

0

= E(Y1 ) = µ1 .

150

6 Models and Methods for Missing Data

Consequently, if either the model for E(Y1 |W ) or P (R = 1|W ) is correctly speciﬁed, then the double robust estimator consistently estimates µ1 . We will demonstrate later that if both models are correctly speciﬁed, then the double robust estimator is more eﬃcient than the IPWCC estimator. Remark 4. Another advantage to the double robust estimator is that, based on some of our experience, this estimator obviates some of the instability problems that can result from small weights as noted in Remark 3 for the IPWCC estimator.

6.6 Exercises for Chapter 6 1. In (6.1) we showed that the sample average of the response variable Y1 among complete cases, when the missingness mechanism is NMAR, will converge in probability to E{Y1 π(Y1 )} , E{π(Y1 )} where π(Y1 ) denotes the probability of being included in the sample as a function of the response variable Y1 (i.e., P (R = 1|Y1 ) = π(Y1 )). If π(y) is an increasing function in y (i.e., the probability of not being missing increases with y), then prove that E{Y1 π(Y1 )} > µ1 . E{π(Y1 )}

7 Missing and Coarsening at Random for Semiparametric Models

7.1 Missing and Coarsened Data In Chapter 6, we described three diﬀerent missing-data mechanisms: 1. MCAR (missing completely at random): The probability of missingness is independent of the data. 2. MAR (missing at random): The probability of missingness depends only on the observed data. 3. NMAR (nonmissing at random): The probability of missingness may also depend on the unobservable part of the data. NMAR is clearly the most problematic. Since missingness may depend on data that are unobserved, we run into nonidentiﬁability problems. Because of these diﬃculties, we do not ﬁnd methods that try to model the missingness mechanism for NMAR models very attractive since the correctness of the model cannot be veriﬁed using the observed data. Another approach, which we believe is more useful, is to use NMAR models as part of a sensitivity analysis. There has been some excellent work along these lines; see, for example, Scharfstein, Rotnitzky, and Robins (1999), Robins, Rotnitzky, and Scharfstein (2000), and Rotnitzky et al. (2001). In this book, we will not consider NMAR models; instead, we will focus our attention on models for missing data that are either MAR or MCAR. Restricting attention only to such models still allows us a great deal of ﬂexibility. Although the primary interest is to make inference on parameters that involve the distribution of Z had Z been observed on the entire sample, the MAR assumption allows us to consider cases where the probability of missingness may also depend on other auxiliary variables W that are collected on the sample. If the reasons for missingness depend on the observed data, including the auxiliary variables, then the MAR assumption may be tenable for a wide variety of problems. We gave an example of this when we introduced the notion of MAR in Chapter 6.

152

7 Missing and Coarsening at Random for Semiparametric Models

Remark 1. Although we have made a distinction between auxiliary variables W and primary variables Z, for ease of notation, we will not introduce additional notation unless absolutely necessary. That is, we can deﬁne the full data Z to be all the data that are collected on individuals in our sample. This may include auxiliary as well as primary variables. For example, the full data Z may be partitioned as (Z1T , Z2T )T , where Z1 are the primary variables and Z2 are the auxiliary variables. The model for the primary variables can be denoted by a semiparametric model pZ1 (z1 , β, η1 ), where β is the parameter of interest and η1 are nuisance parameters. Since the auxiliary variables Z2 are not of primary interest, we might not want to put any additional restrictions on the conditional distribution of the auxiliary variables Z2 given Z1 . This situation can be easily accommodated by considering the semiparametric model pZ (z, β, η), where η = (η1 , η2 ) and pZ (z, β, η) = pZ1 (z1 , β, η1 )pZ2 |Z1 (z2 |z1 , η2 ), where the nuisance function η2 allows for any arbitrary conditional density of Z2 given Z1 . By so doing, auxiliary variables can be introduced as part of a full-data semiparametric model. Again, we emphasize that the underlying objective is to make inference on parameters that describe important aspects of the distribution of the data Z had Z not been missing. That is, had Z been completely observed for the entire sample, then the data would be realizations of the iid random quantities Z1 , . . . , Zn , each with density pZ (z, β, η), where β q×1 is assumed ﬁnitedimensional, and η denotes the nuisance parameter, which for semiparametric models is inﬁnite-dimensional. It is the parameter β in this model that is of primary interest. The fact that some of the data are missing is a diﬃculty that we have to deal with by thinking about and modeling the missingness process. The model for missingness, although important for conducting correct inference, is not of primary inferential interest. Although we have only discussed missing data thus far, it is not any more diﬃcult to consider the more general notion of “coarsening” of data. The concept of coarsened data was ﬁrst introduced by Heitjan and Rubin (1991) and studied more extensively by Heitjan (1993) and Gill, van der Laan, and Robins (1996). When we think of missing data, we generally consider the case where the data on a single individual can be represented by a random vector with, say, l components, where a subset of these components may be missing for some of the individuals in the sample. When we refer to coarsened data, we consider the case where we observe a many-to-one function of Z for some of the individuals in the sample. Just as we allow that diﬀerent subsets of the data Z may be missing for diﬀerent individuals in the sample, we allow the possibility that diﬀerent many-to-one functions may be observed for diﬀerent individuals. Speciﬁcally, we will deﬁne a coarsening (missingness) variable C such that, when “C = r,” we only get to see a many-to-one function of the data, which we denote by Gr (Z), and diﬀerent r correspond to diﬀerent

7.1 Missing and Coarsened Data

153

many-to-one functions. Therefore, C will be a single discrete random variable made up of positive integers r = 1, . . . , and ∞, where denotes the number of diﬀerent many-to-one functions considered. We reserve C = ∞ to denote “no coarsening.” Example 1. A simple illustration of coarsened data is given by the following hypothetical example. An investigator is interested in studying the relationship between the concentration of some biological marker in some ﬁxed volume of an individual’s blood serum and some outcome. However, the investigator is also interested in determining the within-person variability in serum concentrations. Therefore, two blood samples of equal volume are drawn from each individual in a study. Denote by X1 and X2 the serum concentrations for these two samples and by Y the response variable. The full data for this scenario are given as (Y, X1 , X2 ). To save on expense, the investigator measures the concentrations separately on the two samples from only a subset of the patients chosen at random. For the remaining patients, the two blood samples are combined and one measurement is made to obtain the concentration from the combined samples. Since the blood volumes are the same, the combined concentration would be (X1 + X2 )/2. Hence, in this example, there are two levels of coarsening. When C = ∞, we observe the full data (Y, X1 , X2 ) (i.e., G∞ (Y, X1 , X2 ) = (Y, X1 , X2 )) whereas when C = 1, we observe the coarsened data {Y, (X1 + X2 )/2} (i.e., G1 (Y, X1 , X2 ) = {Y, (X1 + X2 )/2}). We now illustrate how missing data is just a special case of the more general concept of coarsening. Missing Data as a Special Case of Coarsening Suppose the full data Z for a single individual is made up of an l-dimensional vector of random variables, say / 0T Z = Z (1) , . . . , Z (l) . Having missing data is equivalent to the case where a subset of the elements of (Z (1) , . . . , Z (l) ) are observed and the remaining elements are missing. This can be represented using the coarsening notation {C, GC (Z)}, where, the many-toone function Gr (Z) maps the vector Z to a subset of elements of this vector whenever C = r. For example, let Z = (Z (1) , Z (2) )T be a vector of two random variables. Deﬁne C GC (Z) 1 Z (1) 2 Z (2) ∞ (Z (1) , Z (2) )T

154

7 Missing and Coarsening at Random for Semiparametric Models

That is, if C = 1, then we only observe Z (1) , and Z (2) is missing; if C = 2, then we observe Z (2) , and Z (1) is missing; and if (C = ∞), then there are no missing data and we observe Z = (Z (1) , Z (2) )T . Remark 2. If we were dealing only with missing data, say, where diﬀerent subsets of an l-dimensional random vector may be missing, it may be more convenient to deﬁne the missingness variable to be an l-dimensional vector of 1’s and 0’s to denote which element of the vector is observed or missing. If it is convenient to switch to such notation, we will use R to denote such missingness indicators. This was the notation used to represent missing data, for example, in Chapter 6. The theory developed in this book will apply to missing and coarsened data problems where it is assumed that there is a positive probability of observing the full data. That is, P (C = ∞|Z = z) ≥ ε > 0

for z a.e.

Therefore, some problems that may be thought of as missing-data problems but where no complete data are ever observed would not be part of the theory we will consider. For example, measurement error problems that do not include a validation set (i.e., when the true underlying covariate is never observed for any individual in our sample but instead only some mismeasured version of the covariate is available) cannot be covered by the theory developed in this book. Coarsened-Data Mechanisms In problems where the data are coarsened or missing, it is assumed that we get to observe the coarsening variable C and the corresponding coarsened data GC (Z). Thus, the observed data are realizations of the iid random quantities {Ci , GCi (Zi )}, i = 1, . . . , n. To specify models for coarsened data, we must specify the probability distribution of the coarsening process together with the probability model for the full data (i.e., the data had there not been any coarsening). As with missingness, we can deﬁne diﬀerent coarsening mechanisms that can be categorized as coarsening completely at random (CCAR), coarsening at random (CAR), and noncoarsening at random (NCAR). These are deﬁned as follows, 1. Coarsening completely at random: P (C = r Z) = (r)

for all r, Z; i.e., C ⊥ ⊥ Z.

7.1 Missing and Coarsened Data

155

2. Coarsening at random: P (C = r Z) = {r, Gr (Z)}. The probability of coarsening depends on Z only as a function of the observed data. 3. Noncoarsening at random: Noncoarsening at random (NCAR) corresponds to models where coarsening at random fails to hold. That is, the probability of coarsening depends on Z, possibly as a function of unobserved parts of Z; i.e., if there exists z1 , z2 such that Gr (z1 ) = Gr (z2 ) for some r and P (C = r|z1 ) = P (C = r|z2 ), then the coarsening is NCAR. As with nonmissing at random (NMAR), when coarsening is NCAR, we run into nonidentiﬁability problems. Therefore, we focus our attention on models where the coarsening mechanism is either CCAR or CAR. When working with coarsened data, we distinguish among three types of data, namely full data, observed data, and complete data, which we now deﬁne: •

Full data are the data Z1 , . . . , Zn that are iid with density pZ (z, β, η) and that we would like to observe. That is, full data are the data that we would observe had there been no coarsening. With such data we could make inference on the parameter β using standard statistical techniques developed for such parametric or semiparametric models. • Because of coarsening, full data are not observed; instead, the observed data are denoted by iid random quantities [{C1 , GC1 (Z1 )}, . . . , {Cn , GCn (Zn )}],

where Ci denotes the coarsening variable and GCi (Zi ) denotes the corresponding coarsened data for the i-th individual in the sample. It is observed data that are available to us for making inference on the parameter β. • Finally, when Ci = ∞, then the data for the i-th individual are not coarsened (i.e., when Ci = ∞, we observe the data Zi ). Therefore, we denote by complete data the data only for individuals i in the sample such that Ci = ∞ (i.e., complete data are {Zi : for all i such that Ci = ∞}). Complete data are often used for statistical analysis in many software packages when there are missing data.

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7 Missing and Coarsening at Random for Semiparametric Models

7.2 The Density and Likelihood of Coarsened Data In order to ﬁnd observed (coarsened)-data estimators of the parameter of interest β using likelihood methods, or, for that matter, in order to derive the underlying semiparametric theory, we need to derive the likelihood of the observed data in terms of the parameter β and other nuisance parameters. To derive the density of the observed data, we ﬁrst consider the unobservable random vectors {(Ci , Zi ), i = 1, . . . , n} assumed iid. We emphasize that these data are unobservable because, when Ci = r, r = ∞, we only get to observe the many-to-one transformation Gr (Zi ) and not Zi itself. Nonetheless, working with such data will make the assumptions necessary to obtain the likelihood of the observed data transparent. It is sometimes convenient to denote the data {(Ci , Zi ), i = 1, . . . , n} as the full data, whereas previously we said that the full data will be deﬁned as {Zi , i = 1, . . . , n}. Therefore, in what follows, we will sometimes refer to full data by {Zi , i = 1, . . . , n} and other times by {(Ci , Zi ), i = 1, . . . , n}, and the distinction should be clear by the context. Since the observed data {C, GC (Z)} are a known function of the full data (C, Z), this means that the density of the observed data is induced by the density of the full data. The density of the full data and the corresponding likelihood, in terms of the parameters β, η, and ψ, are given by pC,Z (r, z, ψ, β, η) = P (C = r|Z = z, ψ)pZ (z, β, η). That is, the density and likelihood of the full data are deduced from the density and likelihood of Z, pZ (z, β, η), and the density and likelihood for the coarsening mechanism (i.e., the probability of coarsening given Z). We emphasize that the density for the coarsening mechanism may also be from a model that is described through the parameter ψ. Remark 3. Since the coarsening variable C is discrete, the dominating measure for C is the counting measure that puts unit mass on each of the ﬁnite values that C can take including C = ∞. The dominating measure for Z is, as before, deﬁned to be νZ (generally the Lebesgue measure for a continuous random variable, the counting measure for a discrete random variable, or a combination when Z is a random vector of continuous and discrete random variables). Consequently, the dominating measure for the densities of (C, Z) is just the product of the counting measure for C by νZ . Discrete Data For simplicity, we will ﬁrst consider the case when Z itself is a discrete random vector. Consequently, the dominating measure is the counting measure over the discrete combinations of C and Z, and integrals with respect to such a

7.2 The Density and Likelihood of Coarsened Data

157

dominating measure are just sums. Although this is overly simplistic, it will be instructive in describing the probabilistic structure of the problem. We will also indicate how this can be generalized to continuous Z as well. Thus, with discrete data, the probability density of the observed data {C, GC (Z)} is derived as P {C = r, GC (Z) = gr } = P (C = r, Z = z) {z:Gr (z)=gr }

=

P (C = r|Z = z)P (Z = z).

{z:Gr (z)=gr }

Remark 4. Rather than developing one set of notation for discrete Z and another set of notation (using integrals) for continuous Z, we will, from now on, use integrals with respect to the appropriate dominating measure. Therefore, when we have discrete Z, and νZ is the corresponding counting measure, we will denote P (Z ∈ A) = P (Z = z) z∈A

as

pZ (z)dνZ (z). z∈A

With this convention in mind, we write the density and likelihood of the observed data, when Z is discrete, as pC,GC (Z) (r, gr , ψ, β, η) = pC,Z (r, z, ψ, β, η)dνZ (z) {z:Gr (z)=gr }

=

P (C = r|Z = z, ψ)pZ (z, β, η)dνZ (z).

(7.1)

{z:Gr (z)=gr }

Continuous Data It will be instructive to indicate how the density of the observed data would be derived if Z was a continuous random vector and the relationship of this density to (7.1). For example, consider the case where Z = (Z1 , . . . , Zl )T is continuous. Generally, Gr (Z) is a dimensional-reduction transformation, unless r = ∞. This is certainly the case for missing-data mechanisms. Let Gr (z) be lr -dimensional, lr < l for r = ∞, and assume there exists a function Vr (z) that is (l − lr )-dimensional so that the mapping z ↔ {GTr (z), VrT (z)}T is one-to-one for all r. Deﬁne the inverse transformation by

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7 Missing and Coarsening at Random for Semiparametric Models

z = Hr (gr , vr ). Then, by the standard formula for change of variables, the density pGr ,Vr (gr , vr ) = pZ {Hr (gr , vr )}J(gr , vr ),

(7.2)

where J is the Jacobian (more precisely, the Jacobian determinant) of Hr with respect to (gr , vr ). If we want to ﬁnd the density of the observed data, namely pC,GC (r, gr ), we can use pC,GC (r, gr ) = pC,GC ,VC (r, gr , vr )dvr , (7.3) where pC,GC ,VC (r, gr , vr ) = P (C = r|Gr = gr , Vr = vr )pGr ,Vr (gr , vr ). Note that P (C = r|Gr = gr , Vr = vr ) = P {C = r|Z = Hr (gr , vr )}.

(7.4)

Consequently, using (7.2) and (7.4), we can write (7.3), including the parameters ψ, β, and η, as pC,GC (r, gr , ψ, β, η) = P {C = r|Z = Hr (gr , vr ), ψ}pZ {Hr (gr , vr ), β, η}J(gr , vr )dvr .

(7.5)

Therefore, the only diﬀerence between (7.1) for discrete Z and (7.5) for continuous Z is the Jacobian that appears in (7.5). Since the Jacobian does not involve parameters in the model, it will not have an eﬀect on the subsequent likelihood. Likelihood when Data Are Coarsened at Random The likelihood we derived in (7.1) was general, as it allowed for any coarsening mechanism, including NCAR. As we indicated earlier, we will restrict attention to coarsening at random mechanisms (CAR), where P (C = r|Z = z) = {r, Gr (z)} for all r, z. Coarsening completely at random (CCAR) is just a special case of this. We remind the reader that another key assumption being made throughout is that P (C = ∞|Z = z) ≥ ε > 0 for all r, z. Now that we have shown how to derive the likelihood of the observed data from the marginal density of the desired data Z and the coarsening mechanism, P (C = r|Z = z), we can now derive the likelihood of the observed data when coarsening is CAR. To do so, we need to consider a model for the coarsening density in terms of parameters. For the time being, we will be very general and denote such a model by

7.2 The Density and Likelihood of Coarsened Data

159

P (C = r|Z = z) = {r, Gr (z), ψ}, where ψ is an unknown parameter that is functionally independent of (β, η), the parameters for the full-data model. Remark 5. The coarsening or missingness of the data can be by design where the probability {r, Gr (z)} is known to the investigator. For such problems, additional parameters ψ are not necessary. In other cases, where coarsening or missingness occur by happenstance, we may introduce parametric models with a ﬁnite-dimensional parameter ψ or semiparametric models with inﬁnitedimensional parameter ψ, where ψ needs to be estimated. The exercise of ﬁnding reasonable and coherent models for {r, Gr (z), ψ}, even under the MAR or CAR assumption, may not be straightforward and may require special considerations. Examples of such models will be given throughout the remainder of the book but, for the time being, it will be assumed that the model for {r, Gr (z), ψ}, as a function of ψ, is known and has been correctly speciﬁed. We now see that the observed data can be viewed as realizations of the iid random quantities {Ci , GRi (Zi )}, i = 1, . . . , n, with density from a statistical model described through the parameter of interest β and nuisance parameters η and ψ. The CAR assumption will allow simpliﬁcation of the likelihood, as we now demonstrate. When data are CAR, the likelihood of the observed data for a single observation given by (7.1) for discrete Z (now considered as functions of (ψ, β, η)) is pC,GC (Z) (r, gr , ψ, β, η) = P (C = r|Z = z, ψ)pZ (z, β, η)dνZ (z) {z:Gr (z)=gr }

=

(r, gr , ψ)pZ (z, β, η)dνZ (z) {z:Gr (z)=gr }

= (r, gr , ψ)

pZ (z, β, η)dνZ (z).

(7.6)

{z:Gr (z)=gr }

Notice that the parameter ψ for the coarsening process separates from the parameters (β, η) that describe the full-data model. Also notice that if Z were continuous and we used formula (7.5) to derive the density, then, under CAR, we obtain

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7 Missing and Coarsening at Random for Semiparametric Models

pC,GC (Z) (r, gr , ψ, β, η) =

(r, gr , ψ)pZ {Hr (gr , vr ), β, η} J(gr , vr ) dvr * +, ⇓ The Jacobian does not involve any of the parameters.

pZ {Hr (gr , vr ), β, η}J(gr , vr )dvr . (7.7)

= (r, gr , ψ) In both (7.6) and (7.7),

pC,GC (Z) (r, gr , ψ, β, η) = (r, gr , ψ)pGr (Z) (gr , β, η).

(7.8)

Brief Remark on Likelihood Methods Because the parameter ψ describing the coarsening mechanism separates from the parameters (β, η) describing the distribution of Z in the observed data likelihood, when the data are coarsened at random, likelihood methods are often proposed for estimating the parameters β and η. That is, suppose we posit a parametric model for full data (i.e., pZ (z, β, η)) and the aim is to estimate the parameter β using coarsened data {Ci , GCi (Zi )}, i = 1, . . . , n. The likelihood for a realization of such data, (ri , gri ), i = 1, . . . , n, as a function of the parameters, when the coarsening mechanism is CAR, is (by (7.8)) equal to n " n " ! ! (ri , gri , ψ) pGri (Z) (gri , β, η) . (7.9) i=1

i=1

Therefore, the MLE for (β, η) only involves maximizing the function n !

pGri (Z) (gri , β, η),

(7.10)

i=1

where pGr (Z) (gr , β, η) =

pZ (z, β, η)dνZ (z). {z:Gr (z)=gr }

Therefore, as long as we believe the CAR assumption, we can ﬁnd the MLE for β and η without having to specify a model for the coarsening process. If the parameter space for (β, η) is ﬁnite-dimensional, this is especially attractive, as the MLE for β, under suitable regularity conditions, is an eﬃcient estimator. Moreover, the coarsening probabilities, subject to the CAR assumption, play no role in either the estimation of β (or η for that matter) or the eﬃciency

7.2 The Density and Likelihood of Coarsened Data

161

of such an estimator. This has a great deal of appeal, as it frees the analyst from making modeling assumptions for the coarsening probabilities. Maximizing functions such as (7.10) to obtain the MLE may involve integrals and complicated expressions that may not be easy to implement. Nevertheless, there has been a great deal of progress in developing optimization techniques involving quadrature or Monte Carlo methods, as well as other maximization routines such as the EM algorithm, which may be useful for this purpose. Since likelihood methods for missing (coarsened) data have been studied in great detail by others, there will be relatively little discussion of these methods in this book. We refer the reader to Allison (2002), Little and Rubin (1987), Schafer (1997), and Verbeke and Molenberghs (2000) for more details on likelihood methods for missing data. Examples of Coarsened-Data Likelihoods Return to Example 1 Let us return to Example 1 of Section 7.1. Recall that in this example two blood samples of equal volume were taken from each of n individuals in a study that measured the blood concentration of some biological marker. Some of the individuals, chosen at random, had concentration measurements made on both samples. These are denoted as X1 and X2 . The remaining individuals had their blood samples combined and only one concentration measurement was made, equaling (X1 +X2 )/2. Although concentrations must be positive, let us, for simplicity, assume that the distribution of these blood concentrations is well approximated by a normal distribution. To assess the variability of these concentrations between and within individuals, we assume that Xj = α + ej , where α is normally distributed with mean µα and variance σα2 , and ej , j = 1, 2 are independently normally distributed with mean zero and variance σe2 , also independent of α. With this representation, σα2 represents the variation between individuals and σe2 the variation within an individual. From this model, we deduce that Z = (X1 , X2 )T follows a bivariate normal distribution with common mean µα and common variance σα2 + σe2 and covariance σα2 . Since the individuals chosen to have their blood samples combined were chosen at random, this is an example of coarsening completely at random (CCAR). Thus P (C = 1|Z) = , where is the probability of being selected in the subsample and P (C = ∞|Z) = 1 − . The data for this example can be represented as realizations of the iid random vectors {Ci , GCi (Zi )}, i = 1, . . . , n, where, if Ci = ∞, then we observe G∞ (Zi ) = (Xi1 , Xi2 ), whereas if Ci = 1, then we observe G1 (Zi ) = (Xi1 + Xi2 )/2. Under the assumptions of the model, µα Xi1 ∼N ,Σ , (7.11) Xi2 µα where

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7 Missing and Coarsening at Random for Semiparametric Models

Σ=

σα2 + σe2 , σα2 . σα2 , σα2 + σe2

It is also straightforward to show that (Xi1 + Xi2 )/2 ∼ N µα , σα2 + σe2 /2 . Consequently, the MLE for (µα , σα2 , σe2 ) is obtained by maximizing the coarseneddata likelihood (7.10), which, for this example, is given by n ! i=1

−1/2

|Σ|

1 exp − {(Xi1 − µα , Xi2 − µα )T 2 I(Ci =∞)

Σ−1 (Xi1 − µα , Xi2 − µα )}

I(Ci =1) {(Xi1 + Xi2 )/2 − µα }2 . (7.12) × (σα2 + σe2 /2)−1/2 exp − 2(σα2 + σe2 /2) We leave the calculation of the MLE for this example as an exercise at the end of the chapter. Although maximizing the likelihood is the preferred method for obtaining estimators for the parameters in ﬁnite-dimensional parametric models of the full data Z, it may not be a feasible approach for semiparametric models when the parameter space of the full data is inﬁnite-dimensional. We illustrate some of the diﬃculties through an example where the parameter of interest is easily estimated using likelihood techniques if the data are not coarsened but where likelihood methods become diﬃcult when the data are coarsened. The logistic regression model Let Y be a binary response variable {0, 1}, and let X be a vector of covariates. A popular model for modeling the probability of response as a function of the covariates X is the logistic regression model where P (Y = 1|X) =

exp(β T X ∗ ) , 1 + exp(β T X ∗ )

where X ∗ = (1, X T )T , allowing us to consider an intercept term. We make no assumptions on X. With full data, (Yi , Xi ), i = 1, . . . , n, the likelihood of a single observation is exp{(β T x∗ )y} pX {x, η(·)}, pY |X (y|x)pX (x) = (7.13) 1 + exp(β T x∗ ) where the parameter η(·) is the inﬁnite-dimensional nuisance function allowing all nonparametric densities for the marginal distribution of X.

7.3 The Geometry of Semiparametric Coarsened-Data Models

163

If we use maximum likelihood to estimate β with full data, then because the parameters β and η(·) separate in (7.13), it suﬃces to maximize n ! exp{(β T Xi∗ )Yi } (7.14) 1 + exp(β T Xi∗ ) i=1 without any regard to the nuisance parameter η. Equivalently, we can derive the maximum likelihood estimator for β by solving the score equations n exp(β T Xi∗ ) ∗ = 0. (7.15) Xi Yi − 1 + exp(β T Xi∗ ) i=1 This was also derived in (4.65). This indeed is the standard analytic strategy for obtaining estimators for β in a logistic regression model implemented in most statistical software packages. If, however, we had coarsened data (CAR), then the likelihood contribution for the part of the likelihood that involves β for a single observation is exp(β T x∗ )y pX {x, η(·)}dνY,X (y, x). (7.16) 1 + exp(β T x∗ ) {(y,x):Gr (y,x)=gr }

Whereas maximizing the likelihood in β in (7.14) for noncoarsened data involved only the parameter β, ﬁnding the MLE for β with coarsened data now involves maximizing a function in both β and the inﬁnite-dimensional parameter η(·) in a likelihood that involves a product over i of terms like (7.16). Such maximization may be much more diﬃcult if not impossible. We will return to this example later. Consequently, it is important to consider alternatives to likelihood methods for estimating parameters with coarsened data. Before providing such alternatives, it is useful to go back to ﬁrst principles and study the geometry of inﬂuence functions of estimators for the parameter β with coarsened data when the coarsening is CAR.

7.3 The Geometry of Semiparametric Coarsened-Data Models The key to deriving the class of inﬂuence functions of RAL estimators for the parameter β and the corresponding geometry with coarsened data is to build on the corresponding theory of inﬂuence functions of estimators for β and its geometry had the data not been coarsened (i.e., with full data). In so doing, we need to distinguish between the geometry of full-data Hilbert spaces and that of observed-data Hilbert spaces. We denote the full-data Hilbert space of all q-dimensional, mean-zero measurable functions of Z with ﬁnite variance equipped with the covariance inner

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7 Missing and Coarsening at Random for Semiparametric Models

product by HF . This is contrasted with the observed-data Hilbert space of all q-dimensional, mean-zero, ﬁnite variance, measurable functions of {C, GC (Z)} equipped with the covariance inner product, which we denote by H (without the superscript F ). In some cases, we may also consider the Hilbert space HCZ of all q-dimensional, mean-zero, ﬁnite variance, measurable functions of (C, Z) equipped with the covariance inner product. We note that HF and H are both linear subspaces of HCZ . Because inﬂuence functions lie in a subspace of H orthogonal to the nuisance tangent space, the key in identifying inﬂuence functions is ﬁrst to ﬁnd the nuisance tangent space and its orthogonal complement. We remind the reader that •

The full-data nuisance tangent space is the mean-square closure of all fulldata parametric submodel nuisance tangent spaces. The full-data nuisance tangent space is denoted by ΛF . • For a full-data parametric submodel pZ (z, β, γ), where β is q-dimensional and γ is r-dimensional, the nuisance score vector is SγF (Z) =

∂ log pZ (Z, β0 , γ0 ) , ∂γ

and the full-data parametric submodel nuisance tangent space is the space spanned by SγF ; namely, '

( B q×r SγF (Z) for all q × r matrices B .

• The class of full-data inﬂuence functions are the elements ϕF (Z) ∈ HF such that (i) ϕF (Z) ∈ ΛF ⊥ (i.e., orthogonal to the full-data nuisance tangent space) T (ii) E{ϕF (Z)SβF (Z)} = I q×q (identity matrix), where SβF =

∂ log pZ (Z, βo , ηo ) . ∂β

• The eﬃcient full-data score is F Seﬀ (Z) = SβF (Z) − Π{SβF (Z)|ΛF },

and the eﬃcient full-data inﬂuence function is −1 F FT F ϕF Seﬀ (Z). eﬀ (Z) = E Seﬀ (Z)Seﬀ (Z) When considering missing or coarsened data, two issues need to be addressed: (i) What is the class of observed-data inﬂuence functions, and how are they related to full-data inﬂuence functions?

7.3 The Geometry of Semiparametric Coarsened-Data Models

165

(ii) How can we characterize the most eﬃcient inﬂuence function and the semiparametric eﬃciency bound for coarsened-data semiparametric models? Both of these, as well as many other issues regarding semiparametric estimators with coarsened data, will be studied carefully over the next several chapters. We remind the reader that observed-data inﬂuence functions of RAL estimators for β must be orthogonal to the observed-data nuisance tangent space, which we denote by Λ (without the superscript F ). Therefore, we will demonstrate how to derive the observed-data nuisance tangent space and its orthogonal complement. When the data are discrete and coarsening is CAR, the observed-data likelihood for a single observation is given by (7.6); namely, pC,GC (Z) (r, gr , ψ, β, η) = (r, gr , ψ) pZ (z, β, η)dνZ (z). {Gr (z)=gr }

A similar expression for continuous variables, involving Jacobians, was given by (7.7). From here on, we will use the representation of likelihood for discrete data, realizing that these results can be easily generalized to continuous variables using Jacobians. The log-likelihood for a single observation is given by log (r, gr , ψ) + log pZ (z, β, η)dνZ (z). (7.17) {Gr (z)=gr }

The coarsened-data likelihood and log-likelihood are functions of the parameters β, η, and ψ, where β is the parameter of interest and hence η and ψ are nuisance parameters. Since the parameters η and ψ separate out in the likelihood for the observed data, we would expect that the nuisance tangent space will be the direct sum of two orthogonal spaces, one involving the space generated by the score vector with respect to ψ, which we denote by Λψ , and the other space generated by the score vector with respect to η, which we denote by Λη . That is, Λ = Λ ψ ⊕ Λη ,

Λψ

⊥ Λη .

(7.18)

We will give a formal proof of (7.18) later. For the remainder of this chapter, we will only consider the space Λη and its complement. When the coarsening of the data is by design, where the coarsening probabilities {r, Gr (z)} are known to the investigator, then there is no need to introduce an additional parameter ψ or the space Λψ . In that case, the observed-data nuisance tangent space Λ is the same as Λη . Examples where the data are missing by design will be given in Section 7.4. We restrict ourselves to this situation for the time being. In the next chapter, we will

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7 Missing and Coarsening at Random for Semiparametric Models

discuss what to do when the coarsening probabilities are not known to us by design and models for {r, Gr (z), ψ}, as a function of the parameter ψ, have to be developed. The Nuisance Tangent Space Associated with the Full-Data Nuisance Parameter and Its Orthogonal Complement The nuisance tangent space The space Λη is deﬁned as the mean-square closure of parametric submodel tangent spaces associated with the nuisance parameter η. Therefore, we begin by ﬁrst considering the parametric submodel for the full-data Z given by pZ (z, β q×1 , γ r×1 ) and compute the corresponding observed-data score vector. Lemma 7.1. The parametric submodel observed-data score vector with respect to γ is given by ' ( (7.19) Sγ (r, gr ) = E SγF (Z)|Gr (Z) = gr . Proof. The log-likelihood for the observed data (at least the part that involves γ), given by (7.17), is ⎫ ⎧ ⎪ ⎪ ⎬ ⎨ pZ (z, β, γ)dνZ (z) . log ⎪ ⎪ ⎭ ⎩ {Gr (z)=gr }

The score vector with respect to γ is ⎡ ⎧ ⎪ ∂ ⎢ ⎨ Sγ (r, gr ) = ⎣log ⎪ ∂γ ⎩

{Gr (z)=gr }

=

⎫⎤ ⎪ ⎬ ⎥ pZ (z, β, γ)dνZ (z) ⎦ β = β 0 ⎪ ⎭ γ = γ0 * +, same as η = η0

∂ pZ (z, β0 , γ0 )dνZ (z) ∂γ

{Gr (z)=gr }

.

(7.20)

pZ (z, β0 , γ0 )dνZ (z) {Gr (z)=gr }

Dividing and multiplying by pZ (z, β0 , γ0 ) in the integral of the numerator of (7.20) yields SγF (z, β0 , γ0 )pZ (z, β0 , γ0 )dνZ (z) {Gr (z)=gr }

. pZ (z, β0 , γ0 )dνZ (z)

{Gr (z)=gr }

7.3 The Geometry of Semiparametric Coarsened-Data Models

167

Hence, ( ' Sγ (r, gr ) = E SγF (Z)|Gr (Z) = gr . Remark 6. Equation (7.19) is a conditional expectation that only involves the conditional probability distribution of Z given Gr (Z) for a ﬁxed value of r. It will be important for the subsequent theoretical development that we compare and contrast (7.19) with the conditional expectation ' ( E SγF (Z)|C = r, GC (Z) = gr . (7.21) In general, (7.21) will not equal (7.19); however, as we will now show, these are equal under the assumption of CAR. Lemma 7.2. When the coarsening mechanism is CAR, then ( ' ( ' Sγ (r, gr ) = E SγF (Z)|Gr (Z) = gr = E SγF (Z)|C = r, GC (Z) = gr . (7.22) Proof. Equation (7.22) will follow if we can prove that pZ|C,GC (Z) (z|r, gr ) = pZ|Gr (Z) (z|gr ),

(7.23)

which is true because when Gr (z) = gr pZ|C,GC (Z) (z|r, gr ) =

pC,Z (r, z) pC,Z (r, u)dνZ (u)

{Gr (u)=gr }

=

pC|Z (r|z)pZ (z) pC|Z (r|u)pZ (u)dνZ (u)

{Gr (u)=gr }

=

(r, gr )pZ (z) (r, gr ) pZ (u)dνZ (u)

(7.24)

{Gr (u)=gr }

=

pZ (z)

= pZ|Gr (Z) (z|gr ).

(7.25)

pZ (u)dνZ (u) {Gr (v)=gr }

Remark 7. In order to prove (7.23), it was necessary that (r, gr ) cancel in the numerator and denominator of (7.24), which is only true because of CAR.

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7 Missing and Coarsening at Random for Semiparametric Models

Consequently, when the coarsening mechanism is CAR, the corresponding observed-data nuisance score vector for the parametric submodel pZ (z, β, γ) is ' ( Sγ {C, GC (Z)} = E SγF (Z)|C, GC (Z) . (7.26) We are now in a position to deﬁne the observed-data nuisance tangent space associated with the full-data nuisance parameter η. Theorem 7.1. The space Λη (i.e., the mean square closure of parametric submodel nuisance tangent spaces spanned by Sγ {C, GC (Z)}) is the space of elements ' ( (7.27) Λη = E αF (Z)|C, GC (Z) for all αF ∈ ΛF , where ΛF denotes the full-data nuisance tangent space. We will also denote this space by the shorthand notation ' ( Λη = E ΛF |C, GC (Z) . Proof. Using (7.26), we note that the linear subspace, within H, spanned by the parametric submodel score vector Sγ {C, GC (Z)} is ( q×r ' F E Sγ (Z)|C, GC (Z) for all B q×r B ( ' = E B q×r SγF (Z)|C, GC (Z) for all B q×r . The linear subspace Λη consisting of elements B q×r E{SγF (Z)|C, GC (Z)} for some parametric submodel or a limit (as n → ∞) of elements F (Z)|C, GC (Z)} Bnq×rn E{Sγn

for a sequence of parametric submodels and conformable matrices. This is the same as the space consisting of elements ( ' E B q×r SγF (Z)|C, GC (Z) or limits of elements ( ' F (Z)|C, GC (Z) . E Bnq×rn Sγn F But the space of elements B q×r SγF (Z) or limits of elements Bnq×rn Sγn (Z) for parametric submodels is precisely the deﬁnition of the full-data nuisance tangent space ΛF . Consequently, the space Λη can be characterized as the space of elements ' ( Λη = E αF (Z)|C, GC (Z) for all αF ∈ ΛF .

In the special case where data are missing or coarsened by design (i.e., when there are no additional parameters ψ necessary to deﬁne a model for the coarsening probabilities), then the observed-data nuisance tangent space is Λ = Λη . We know that an inﬂuence function of an observed-data RAL estimator for β must be orthogonal to Λ. Toward that end, we now characterize the space orthogonal to Λη (i.e., Λ⊥ η ).

7.3 The Geometry of Semiparametric Coarsened-Data Models

169

The orthogonal complement of the nuisance tangent space q×1 Lemma 7.3. The space Λ⊥ {C, GC (Z)} ∈ H such η consists of all elements h that E[h{C, GC (Z)}|Z] ∈ ΛF ⊥ ,

where ΛF ⊥ is the space orthogonal to the full-data nuisance tangent space. q×1 Proof. The space Λ⊥ {C, GC (Z)} ∈ H that are η consists of all elements h orthogonal to Λη . By Theorem 7.1, this corresponds to the set of elements h(·) ∈ H such that E hT {C, GC (Z)}E{αF (Z)|C, GC (Z)} = 0

for all αF (Z) ∈ ΛF .

(7.28)

Using the law of iterated conditional expectations repeatedly, we obtain the following relationship for (7.28): 0 = E E[hT {C, GC (Z)}αF (Z)|C, GC (Z)] = E[hT {C, GC (Z)}αF (Z)] = E E[hT {C, GC (Z)}αF (Z)|Z] = E E[hT {C, GC (Z)}|Z] αF (Z) for all αF (Z) ∈ ΛF .

(7.29)

Thus (7.29) implies that h{C, GR (Z)} ∈ H belongs to Λ⊥ η if and only if E[h{C, GC (Z)}|Z] is orthogonal to every element αF (Z) ∈ ΛF ; i.e., that E[h{C, GC (Z)}|Z] ∈ ΛF ⊥ . To explore this relationship further, it will be convenient to introduce the notion of a mapping, and more speciﬁcally a linear mapping, from one Hilbert space to another Hilbert space. Deﬁnition 1. A mapping, also sometimes referred to as an operator, K, is a function that maps each element of some linear space to an element of another linear space. In all of our applications, the linear spaces will be well-deﬁned Hilbert spaces. So, for example, if H(1) and H(2) denote two Hilbert spaces, then the mapping K : H(1) → H(2) means that for any h ∈ H(1) , K(h) ∈ H(2) . A linear mapping also has the property that K(ah1 + bh2 ) = aK(h1 ) + bK(h2 ) for any two elements h1 , h2 ∈ H(1) and any scalar constants a and b. A manyto-one mapping means that more than one element h ∈ H(1) will map to the same element in H(2) . For more details regarding linear operators, we refer the reader to Chapter 6 of Luenberger (1969). Deﬁne the many-to-one mapping

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7 Missing and Coarsening at Random for Semiparametric Models

K : H → HF to be K(h) = E[h{C, GC (Z)}|Z]

(7.30)

for h ∈ H. Because of the linear properties of conditional expectations, the mapping K, given by (7.30), is a linear mapping or linear operator. By Lemma 7.3, the space Λ⊥ η can be deﬁned as −1 Λ⊥ (ΛF ⊥ ), η =K

where K−1 is the inverse operator. Deﬁnition 2. Inverse operator For any element hF ∈ HF , K−1 (hF ) corresponds to the set of all elements (assuming at least one exists) h ∈ H such that K(h) = hF . Similarly, the space K−1 (ΛF ⊥ ) corresponds to all elements of h ∈ H such that K(h) ∈ ΛF ⊥ . Since K is a linear operator and ΛF ⊥ is a linear subspace of HF , it is easy to show that K−1 (ΛF ⊥ ) is a linear subspace of H. −1 Let us consider the construction of the space Λ⊥ (ΛF ⊥ ) element by η =K ∗F F⊥ element. Consider a single element ϕ (Z) ∈ Λ . In the following theorem, we show how the inverse K−1 (ϕ∗F ) is computed. Remark 8. Notation convention When we refer to elements of the space ΛF ⊥ , we will use the notation ϕ∗F (Z). This is in contrast to the notation ϕF (Z) (without the ∗), which we use to denote a full-data inﬂuence function. The space perpendicular to the fulldata nuisance tangent space ΛF ⊥ is the space in which the class of full-data inﬂuence functions belongs. In order to be a full-data inﬂuence function, an FT (Z)} = I q×q . element of ΛF ⊥ must also satisfy the property that E{ϕF (Z)Seﬀ We remind the reader that, for any ϕ∗F (Z) ∈ ΛF ⊥ , we can construct an inﬂuence function that equals ϕ∗F (Z), up to a multiplicative constant, by taking −1 FT ϕF (Z) = E{ϕ∗F (Z)Seﬀ (Z)} ϕ∗F (Z). Lemma 7.4. For any ϕ∗F (Z) ∈ ΛF ⊥ , let K−1 {ϕ∗F (Z)} denote the space of ˜ elements h{C, GC (Z)} ∈ H such that ˜ ˜ K[h{C, GC (Z)}] = E[h{C, GC (Z)}|Z] = ϕ∗F (Z). If we could identify any element h{C, GC (Z)} such that K(h) = ϕ∗F (Z), then

7.3 The Geometry of Semiparametric Coarsened-Data Models

171

K−1 {ϕ∗F (Z)} = h{C, GC (Z)} + Λ2 , where Λ2 is the linear subspace in H consisting of elements L2 {C, GC (Z)} such that E[L2 {C, GC (Z)}|Z] = 0; that is, Λ2 = K−1 (0). ˜ Proof. The proof is straightforward. If h{C, GR (Z)} is an element of the space ˜ h{C, GR (Z)}+Λ2 , then h{C, GR (Z)} = h{C, GC (Z)}+L2 {R, GC (Z)} for some L2 {C, GR (Z)} ∈ Λ2 , in which case ˜ E[h{C, GC (Z)}|Z] = E[h{C, GC (Z)}|Z] + E[L2 {C, GC (Z)}|Z] = ϕ∗F (Z) + 0 = ϕ∗F (Z). ˜ Conversely, if E[h{C, GC (Z)}|Z] = ϕ∗F (Z), then ˜ ˜ h{C, GC (Z)} = h{C, GC (Z)} + [h{C, GC (Z)} − h{C, GC (Z)}], ˜ where clearly [h{C, GC (Z)} − h{C, GC (Z)}] ∈ Λ2 . −1 Therefore, in order to construct Λ⊥ (ΛF ⊥ ), we must, for each η = K F⊥ ϕ (Z) ∈ Λ , ∗F

(i) identify one element h{C, GC (Z)} such that E[h{C, GC (Z)}|Z] = ϕ∗F (Z), and (ii) ﬁnd Λ2 = K−1 (0). We now derive the space Λ⊥ η in the following theorem. Theorem 7.2. Under the assumption that E{I(C = ∞)|Z} = (∞, Z) > 0

for all Z (a.e.),

(7.31)

the space Λ⊥ η consists of all elements that can be written as I(C = ∞)ϕ∗F (Z) + (∞, Z)

(7.32)

⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z)}L2r {Gr (Z)}⎦ − I(C = r)L2r {Gr (Z)}, (∞, Z) r=∞

r=∞

where, for r = ∞, L2r {Gr (Z)} is an arbitrary q × 1 measurable function of Gr (Z) and ϕ∗F (Z) is an arbitrary element of ΛF ⊥ .

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7 Missing and Coarsening at Random for Semiparametric Models

Proof. In accordance with the proof of Lemma 7.4, we begin by: (i) Identifying h such that E[h{C, GC (Z)}|Z] = ϕ∗F (Z) A single solution to the equation E[h{C, GC (Z)}|Z] = ϕ∗F (Z) is motivated by the idea of an inverse probability weighted complete-case estimator, which was ﬁrst introduced in Section 6.4. Recall that C = ∞ denotes the case when the data Z are completely observed and (∞, Z) = P (C = ∞|Z). Now consider h{C, GC (Z)} to be I(C = ∞)ϕ∗F (Z) . (∞, Z) This is clearly a function of the observed data. Moreover, I(C = ∞)ϕ∗F (Z) ϕ∗F (Z) E Z = E{I(C = ∞)|Z} = ϕ∗F (Z), (∞, Z) (∞, Z) where, in order for the equation above to hold, we must make sure we are not dividing 0 by 0; hence the need for assumption (7.31). −1 Consequently, Λ⊥ (ΛF ⊥ ) can be written as the direct sum of two η = K linear subspaces; namely, Λ⊥ η =

I(C = ∞)ΛF ⊥ ⊕ Λ2 , (∞, Z)

(7.33)

which is the linear subspace of H with elements I(C = ∞)ϕ∗F (Z) ⊥ Λη = + L2 {C, GC (Z)} : ϕ∗F (Z) ∈ ΛF ⊥ , (∞, Z) L2 {C, GC (Z)} ∈ Λ2 ;

i.e., E[L2 {C, GC (Z)}|Z] = 0 .

(7.34)

To complete the proof, we need to derive the linear space Λ2 . (ii) The space Λ2 = K−1 (0) Because we are assuming that the coarsening variable C is discrete, we can express any function h{C, GR (Z)} ∈ H as I(C = r)hr {Gr (Z)}, (7.35) I(C = ∞)h∞ (Z) + r=∞

where h∞ (Z) denotes an arbitrary q × 1 function of Z and hr {Gr (Z)} denotes an arbitrary q × 1 function of Gr (Z). The space of functions L2 {C, GC (Z)} ∈ Λ2 ⊂ H must satisfy

7.3 The Geometry of Semiparametric Coarsened-Data Models

173

E[L2 {C, GC (Z)}|Z] = 0; that is, ⎡ E ⎣I(C = ∞)L2∞ (Z) +

⎤ I(C = r)L2r {Gr (Z)} Z ⎦ = 0,

r=∞

or (∞, Z)L2∞ (Z) +

{r, Gr (Z)}L2r {Gr (Z)} = 0.

(7.36)

r=∞

Consequently, to obtain an arbitrary element of L2 {C, GC (Z)} ∈ Λ2 , we can deﬁne any set of q-dimensional measurable functions L2r {Gr (Z)}, r = ∞, and, for any such set of functions, (7.36) will be satisﬁed by taking L2∞ (Z) = −{(∞, Z)}−1 {r, Gr (Z)}L2r {Gr (Z)}, r=∞

where, again, assumption (7.31) is needed to guarantee that we are not dividing by zero. Hence, for any L2r {Gr (Z)}, r = ∞, we can deﬁne a typical element of Λ2 as ⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z)}L2r {Gr (Z)}⎦ − I(C = r)L2r {Gr (Z)}. (∞, Z) r=∞

r=∞

(7.37) Combining the results from (7.34) and (7.37), we are now able to explicitly deﬁne the linear space Λ⊥ η to be that consisting of all elements given by (7.32). Identifying the space Λ⊥ will often guide us in deriving semiparametric estimators. When data are coarsened by design, Λ⊥ = Λ⊥ η. Remark 9. The representation of Λ⊥ η given by(7.33) as a direct sum of two linear spaces will give us insight on how to construct estimating equations whose solution will yield semiparametric RAL estimators for β with coarsened data. In Chapters 4 and 5, we showed how to use elements of ΛF ⊥ (i.e., the space orthogonal to the full-data nuisance tangent space) to construct estimating equations whose solution resulted in full-data RAL estimators for β. Since F⊥ the ﬁrst space in the direct sum (7.33), namely I(C=∞)Λ (∞,Z) , consists of the inverse probability weighted complete-case elements of ΛF ⊥ , this suggests that observed-data estimators for β can be constructed by using inverse probability weighted complete-case (IPWCC) full-data estimating equations. This would lead to what are called IPWCC estimators. We gave a simple example of this in Section 6.4.

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7 Missing and Coarsening at Random for Semiparametric Models

The second space, Λ2 in (7.33) will be referred to as the augmentation space. Estimators for β that include elements of Λ2 as part of the estimator will be referred to as augmented inverse probability weighted complete-case (AIPWCC) estimators. In Section 6.5, we introduced such an estimator and showed how the augmentation term can help us gain eﬃciency and, in some cases, leads to estimators with the property of double robustness. Therefore, we will formally deﬁne the two linear subspaces as follows. Deﬁnition 3. The linear subspace contained in H consisting of elements I(C = ∞)ϕ∗F (Z) ; for all ϕ∗F (Z) ∈ ΛF ⊥ , (∞, Z) also denoted as

I(C=∞)ΛF ⊥ (∞,Z) ,

will be deﬁned to be the IPWCC space.

Deﬁnition 4. The linear space Λ2 ⊂ H will be deﬁned to be the augmentation space. Before continuing to more complicated situations, it will be instructive to see how the geometry we have developed so far will aid us in constructing estimators in several examples when data are missing at random by design.

7.4 Example: Restricted Moment Model with Missing Data by Design Consider the semiparametric restricted moment model that assumes that E(Y |X) = µ(X, β), where Y is the response variable and X is a vector of covariates. Here, Z = (Y, X) denotes full data. We studied the semiparametric properties of this model in great detail in Sections 4.5 and 4.6, where we also showed, in (4.48), that a typical element of ΛF ⊥ is given as A(X){Y − µ(X, β0 )}. This motivates the generalized estimating equation (GEE), or m-estimator, which is the solution to n

A(Xi ){Yi − µ(Xi , β)} = 0

(7.38)

i=1

using a sample of data (Yi , Xi ), i = 1, . . . , n. Suppose, by design, we coarsen the data at random. For example, let the vector of covariates X for a single observation be partitioned into two sets of

7.4 Example: Restricted Moment Model with Missing Data by Design T

175

T

variables, X = (X (1) , X (2) )T , where X (1) are variables that are relatively inexpensive to collect, whereas X (2) are expensive to collect. For example, X (2) may be genetic markers that are expensive to process, whereas X (1) may be descriptive variables such as age, race, sex, etc. In such a case, we might decide to collect the response variable Y and the inexpensive covariates X (1) on everyone in the sample but collect the expensive covariates X (2) only on a subset of individuals. Moreover, we let the probability of collecting X (2) depend on the values of Y and X (1) . This might be the case if, say, we want to overrepresent some values of Y and X (1) in the subset where all the data are collected. This is an example of missing data by design. That is, the full data (1) (2) (1) are denoted by Zi = (Yi , Xi , Xi ), i = 1, . . . , n. Yi and Xi are observed on (2) everyone, whereas Xi may be missing for some individuals. To implement (1) such a design, we would collect the data (Yi , Xi ) for all patients i = 1, . . . , n, as well as blood samples that could be used to obtain the expensive genetic markers. For patient i we then choose, at random, the complete-case binary (1) indicator Ri taking the value 1 or 0 with probability π(Yi , Xi ) and 1 − (1) π(Yi , Xi ) respectively, where the function 0 < π(y, x(1) ) ≤ 1 is a known function of the response Y = y and the covariates X (1) = x(1) chosen by the investigator. If Ri = 1, then we process the blood sample and obtain the (2) genetic markers Xi ; otherwise, we let that data be missing. Since there are only two levels of coarsening in this problem, it is convenient to work with the binary indicator R to denote whether the observation was complete or whether some of the data (X (2) in this case) were missing. The relationship to the notation we have been using is as follows: R = (0, 1), where R is not scripted, is equivalent to the coarsening variable C = (1, ∞), G1 (Z) = (Y, X (1) ), G∞ (Z) = Z = (Y, X (1) , X (2) ), P (C = 1|Z) = {1, G1 (Z)} = 1 − π(Y, X (1) ), and P (C = ∞|Z) = {∞, G∞ (Z)} = π(Y, X (1) ). Note 1. On notation for missingness probabilities In keeping with much of the notation in the literature, we denote the probability of a complete case by P (R = 1|Y, X (1) ) as π(Y, X (1) ). This should not be confused with coarsening probabilities, which are denoted as P (C = r|Z) = {r, Gr (Z)}. Since the missingness probabilities are known by design for this example, the nuisance tangent space for the observed data is Λη , and the space orthogonal to the nuisance tangent space, Λ⊥ η , derived in (7.32) of Theorem 7.2, is Rϕ∗F (Z) ∗F F⊥ + L {C, G (Z)} : ϕ (Z) ∈ Λ , L {C, G (Z)} ∈ Λ 2 C 2 C 2 . (7.39) π(Y, X (1) ) Because L21 {G1 (Z)} is an arbitrary q × 1 function of (Y, X (1) ), which we denote by L(Y, X (1) ), we can use formula (7.37) to show, after some algebra, that any element L2 {C, GC (Z)} ∈ Λ2 can be expressed as

176

7 Missing and Coarsening at Random for Semiparametric Models

R − π(Y, X (1) ) π(Y, X (1) )

L(Y, X (1) ).

(7.40)

Since a typical element ϕ∗F (Z) ∈ ΛF ⊥ for the restricted moment model is A(X){Y − µ(X, β0 )}, for arbitrary A(X), then by (7.39) and (7.40), a typical element of Λ⊥ η is R[A(X){Y − µ(X, β0 )}] + π(Y, X (1) )

R − π(Y, X (1) ) π(Y, X (1) )

L(Y, X (1) )

for arbitrary A(X) and L(Y, X (1) ). We have shown that identifying elements orthogonal to the nuisance tangent space and using these as estimating functions (i.e., functions of the data and the parameter) may guide us in constructing estimating equations whose solution would yield a consistent, asymptotically normal estimator for β. Therefore, for this problem, we might consider estimating β with a sample of coarsened data (1)

(2)

(Ri , Yi , Xi , Ri Xi ),

i = 1, . . . , n,

by using the m-estimator that solves # n Ri A(Xi ){Yi − µ(Xi , β)} (1) i=1 π(Yi , Xi ) " $ (1) Ri − π(Yi , Xi ) (1) L(Yi , Xi ) = 0. + (1) π(Yi , Xi )

(7.41)

If this estimator is to be consistent, at the least we would need that, at the truth, R − π(Y, X (1) ) R (1) A(X){Y − µ(X, β L(Y, X )} + ) = 0. E 0 π(Y, X (1) ) π(Y, X (1) ) Using the law of iterated conditioning, where we ﬁrst condition on Y, X, we obtain A(X){Y − µ(X, β0 )} E E(R|Y, X) π(Y, X (1) ) E(R|Y, X) − π(Y, X (1) ) (1) L(Y, X ) . (7.42) + π(Y, X (1) ) Since

E(R|Y, X) = P (R = 1|Y, X) = P (R = 1|Y, X (1) , X (2) ),

which, by design, equals π(Y, X (1) ), we obtain that (7.42) is equal to

7.4 Example: Restricted Moment Model with Missing Data by Design

E[A(X){Y − µ(X, β0 )} + 0] = 0.

177

(7.43)

Also, the usual expansion of m-estimators can be used to derive asymptotic normality. That is, 0 n

#

Ri

ˆ

(1)

Ri − π(Yi , Xi )

$

"

(1) L(Yi , Xi )

A(Xi ){Yi − µ(Xi , βn )} + (1) (1) π(Yi , Xi ) i=1 π(Yi , Xi ) " # $ n (1) Ri Ri − π(Yi , Xi ) (1) L(Yi , Xi ) = A(Xi ){Yi − µ(Xi , β0 )} + (1) (1) π(Yi , Xi ) i=1 π(Yi , Xi ) $ # n Ri − A(Xi )D(Xi , βn∗ ) (βˆn − β0 ), (1) π(Y , X ) i i=1 i =

where D(X, β) = ∂µ(X, β)/∂β T and βn∗ is an intermediate value between βˆn and β0 . Therefore, # $−1 n Ri 1/2 ˆ −1 ∗ n (βn − β0 ) = n A(Xi )D(Xi , βn ) (1) i=1 π(Yi , Xi ) # n Ri −1/2 ×n A(Xi ){Yi − µ(Xi , β0 )} (1) i=1 π(Yi , Xi ) " $ (1) Ri − π(Yi , Xi ) (1) L(Yi , Xi ) . + (1) π(Yi , Xi ) Under suitable regularity conditions, " Ri R P −1 ∗ n A(X)D(X, β0 ) . →E A(Xi )D(Xi , βn ) − (1) π(Y, X (1) ) π(Yi , Xi ) Using iterated conditioning, where ﬁrst we condition on Y, X, we obtain E{A(X)D(X, β0 )}. Consequently, n

1/2

#

(βˆn − β0 ) = n−1/2

n

[E{A(X)D(X, β0 )}]−1

i=1

Ri (1)

π(Yi , Xi )

A(Xi ){Yi − µ(Xi , β0 )} +

(1)

Ri − π(Yi , Xi ) (1)

π(Yi , Xi )

+ op (1). Therefore, the i-th inﬂuence function for βˆn is

"

$ (1) L(Yi , Xi )

(7.44)

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7 Missing and Coarsening at Random for Semiparametric Models

# −1

[E{A(X)D(X, β0 )}]

Ri

A(Xi ){Yi − µ(Xi , β0 )} (1) π(Yi , Xi ) $ " (1) Ri − π(Yi , Xi ) (1) + L(Yi , Xi ) , (1) π(Yi , Xi )

which we demonstrated has mean zero, in (7.42) and (7.43), using iterated conditional expectations. We note that this inﬂuence function is proportional to the element in Λ⊥ η that motivated the corresponding m-estimator. Also, because this estimator is asymptotically linear, as shown in (7.44), we immediately deduce that this estimator is asymptotically normal with asymptotic variance being the variance of its inﬂuence function. Other than regularity conditions, no assumptions were made on the distribution of (Y, X), beyond that of the restricted moment assumption, to obtain asymptotic normality. Therefore, the resulting estimator is a semiparametric estimator. Standard methods using a sandwich variance can be used to derive an estimator for the asymptotic variance of βˆn , the solution to (7.41). Such a sandwich estimator was derived for the full-data GEE estimator in (4.9) of Section 4.1. We leave the details as an exercise at the end of the chapter. Hence, for the restricted moment model with missing data that are missing by design, we have derived the space orthogonal to the nuisance tangent space (i.e., Λ⊥ η ) and have constructed an m-estimator with inﬂuence function proportional to any element of Λ⊥ η . Since all inﬂuence functions of RAL estimators for β must belong to Λ⊥ η , this means that any RAL estimator for β must be asymptotically equivalent to one of the estimators given by the solution to (7.41). Remark 10. The estimator for β, given as the solution to (7.41), is referred to as an augmented inverse probability weighted complete-case (AIPWCC) estimator. If L(Y, X (1) ) is chosen to be identically equal to zero, then the estimating equation in (7.41) becomes n

Ri

i=1

π(Yi , Xi )

(1)

A(Xi ){Yi − µ(Xi , β)} = 0.

(7.45)

The solution to (7.45) is referred to as an inverse probability weighted complete-case (IPWCC) estimator. The second term in (7.41), which involves the arbitrary function L(Y, X (1) ), allows contributions from individuals with missing data into the estimating equation. Properly chosen augmentation will result in an estimator with greater eﬃciency. The choice of the inﬂuence function and hence the corresponding class of estimators depends on the arbitrary functions A(X) and L(Y, X (1) ). With full data, the class of estimating equations is characterized by (7.38). This

7.4 Example: Restricted Moment Model with Missing Data by Design

179

requires us to choose the function A(X). In Chapter 4, we proved that the optimal choice for A(X) was DT (X)V −1 (X), where V (X) = var (Y |X), and suggested adaptive strategies for ﬁnding locally eﬃcient estimators for β in Section 4.6. With missing data by design, we also want to ﬁnd the optimal RAL estimator for β; i.e., the RAL estimator for β with the smallest asymptotic variance. This means that we must derive the functions A(X) and L(Y, X (1) ), which yields an estimator in (7.41) with the smallest asymptotic variance. Finding the optimal estimator with coarsened data will require special considerations that will be the focus of later chapters. In general, the optimal choice of A(X) with coarsened data is not necessarily the same as it is for full data. These issues will be studied more carefully. The Logistic Regression Model We gave an example in Section 7.2 where we argued that with coarsened data it was diﬃcult to obtain estimators for β using likelihood methods. Speciﬁcally, we considered the logistic regression model for the probability of response Y = 1 as a function of covariates X, where Y denotes a binary response variable. Let us consider the likelihood for such a model if we had missing T T data by design as described above; that is, where X = (X (1) , X (2) )T and where we always observe Y and X (1) on everyone in the sample but only observe X (2) on a subset chosen at random with probability π(Y, X (1) ) by design. Also, to allow for an intercept term in the logistic regression model, T T T we deﬁne X ∗ = (1, X (1) , X (2) )T and X (1∗) = (1, X (1) )T . The density of the full data (Y, X) for this problem can be written as pY,X (y, x, β, η1 , η2 ) = pY |X (y|x, β)pX (2) |X (1) (x(2) |x(1) , η1 )pX (1) (x(1) , η2 ) exp{(β1T x(1∗) + β2T x(2) )y} p (2) (1) (x(2) |x(1) , η1 )pX (1) (x(1) , η2 ), = 1 + exp(β1T x(1∗) + β2T x(2) ) X |X where β is partitioned as β = (β1T , β2T )T , pX (2) |X (1) (x(2) |x(1) , η1 ) denotes the conditional density of X (2) given X (1) , speciﬁed through the parameter η1 , and pX (1) (x(1) , η2 ) denotes the marginal density of X (1) , speciﬁed through the parameter η2 . Because the parameter of interest β separates from the parameters η1 and η2 in the density above, ﬁnding the MLE for β with full data only involves maximizing the part of the likelihood above involving β and is easily implemented in most software packages. In contrast, the density of the observed data (R, Y, X (1) , RX (2) ) is given by

180

7 Missing and Coarsening at Random for Semiparametric Models

{pY |X (y|x, β)}r {pX (2) |X (1) (x(2) |x(1) , η1 )}r 1−r × pY |X (y|x, β)pX (2) |X (1) (x(2) |x(1) , η1 )dνX (2) (x(2) ) pX (1) (x(1) , η2 ) r exp{(β1T x(1∗) + β2T x(2) )y} {pX (2) |X (1) (x(2) |x(1) , η1 )}r 1 + exp(β1T x(1∗) + β2T x(2) ) exp{(β1T x(1∗) + β2T x(2) )y} × 1 + exp(β1T x(1∗) + β2T x(2) ) 1−r pX (2) |X (1) (x(2) |x(1) , η1 )dνX (2) (x(2) )

=

(7.46)

(7.47)

× pX (1) (x(1) , η2 ). Because the parameters β and η1 do not separate in the density above, deriving the MLE for β involves maximizing, as a function of β and η1 , the product (over i = 1, . . . , n) of terms (7.46) × (7.47). Even if we were willing to make simplifying parametric assumptions about the conditional distribution of X (2) given X (1) in terms of a ﬁnite number of parameters η1 , this would be a complicated maximization, but if we wanted to be semiparametric (i.e., put no restrictions on the conditional distribution of X (2) given X (1) ), then this problem would be impossible as it would suﬀer from the curse of dimensionality. Notice that in the likelihood formulation above, nowhere do the probabilities π(Y, X (1) ) come into play, even though they are known to us by design. Since the logistic regression model is just a simple example of a restricted moment model, estimators for the parameter β for the semiparametric model, which puts no restrictions on the joint distribution of (X (1) , X (2) ), can be found easily by solving the estimating equation (7.41), where µ(Xi , β) = exp(β T Xi∗ )/{1 + exp(β T Xi∗ )} and for some choice of A(X) and L(Y, X (1) ). With no missing data, we showed in (4.65) that the optimal choice for A(X) is X ∗ . Consequently, one easy way of obtaining an estimator for β is (1) by solving (7.41) using A(Xi ) = Xi∗ and L(Yi , Xi ) = 0, leading to the estimating equation n

Ri

i=1

π(Yi , Xi )

(1)

Xi∗ Yi −

exp(β T Xi∗ ) 1 + exp(β T Xi∗ )

= 0.

(7.48)

This estimator is an inverse probability weighted complete case (IPWCC) estimator for β. Although this estimator is a consistent, asymptotically normal semiparametric estimator for β, it is by no means eﬃcient. Since this estimator only uses the complete cases (i.e., the data from individual i : Ri = 1), it is intuitively clear that additional eﬃciency can be gained by using the data from individuals i : Ri = 0, where only some of the data are missing. Therefore, it would be preferable to use an AIPWCC estimator given by (7.41); namely,

7.5 Recap and Review of Notation n

exp(β T Xi∗ ) ∗ Y − X i i (1) 1 + exp(β T Xi∗ ) i=1 π(Yi , Xi ) " (1) Ri − π(Yi , Xi ) (1) L(Y + , X ) = 0, i i (1) π(Yi , Xi ) Ri

181

(7.49)

with some properly chosen L(Y, X (1) ). This illustrates the usefulness of understanding the semiparametric theory for missing and coarsened data. Of course, the choice of A(X) and L(Y, X (1) ) that will result in eﬃcient estimators for β still needs to be addressed.

7.5 Recap and Review of Notation Before continuing, we believe it is worthwhile to review some of the basic ideas and notation that have been developed thus far. Full data •

Full data are denoted by Z with density from a semiparametric model pZ (z, β, η), where β denotes the q-dimensional parameter of interest and η denotes the inﬁnite-dimensional nuisance parameter. • HF denotes the full-data Hilbert space deﬁned as all mean-zero, qdimensional measurable functions of Z with ﬁnite variance equipped with the covariance inner product. • ΛF is the full-data nuisance tangent space spanned by the full-data nuisance score vectors for parametric submodels and their mean-square closure. • ΛF ⊥ = {set of elements ϕ∗F (Z) that are orthogonal to ΛF }. This is the space on which inﬂuence functions lie. Identifying this space helps motivate full-data m-estimators. Observed (coarsened) data •

Coarsened data are denoted by {C, GC (Z)}, where the coarsening variable C is a discrete random variable taking on values 1, . . . , and ∞. When C = r, r = 1, . . . , , then we observe the many-to-one transformation Gr (Z). C = ∞ is reserved to denote complete data; i.e., G∞ (Z) = Z. • We distinguish among three types of coarsening mechanisms: – Coarsening completely at random (CCAR): The coarsening probabilities do not depend on the data. – Coarsening at random (CAR): The coarsening probabilities only depend on the data as a function of the observed data. – Noncoarsening at random (NCAR): The coarsening probabilities depend on the unobserved part of the data.

182

•

7 Missing and Coarsening at Random for Semiparametric Models

When coarsening is CAR, we denote the coarsening probabilities by P (C = r|Z) = {r, Gr (Z)}.

•

A key assumption is that there is a positive probability of observing complete data; that is, P (C = ∞|Z = z) = (∞, Z) > > 0 for all z

in the support of Z. • H denotes the observed-data Hilbert space of q-dimensional, mean-zero, ﬁnite-variance, measurable functions of {C, GC (Z)} equipped with the covariance inner product. • Because C takes on a ﬁnite set of values, a typical function h{C, GC (Z)} can be written as h{C, GC (Z)} = I(C = ∞)h∞ (Z) + I(C = r)hr {Gr (Z)}. r=∞

•

The observed-data nuisance tangent space Λ = Λ ψ ⊕ Λη , Λψ ⊥ Λη , where Λψ is spanned by the score vector with respect to the parameter ψ used to describe the coarsening process and Λη is spanned by the observeddata nuisance score vectors for parametric submodels and their meansquare closures. Speciﬁcally, Λη = E{αF (Z)|C, GC (Z)} : αF (Z) ∈ ΛF = E{ΛF |C, GC (Z)}.

•

In this chapter, we did not consider models for the coarsening probabilities; rather, we assumed they are known by design. Therefore, we didn’t need to consider the space Λψ , in which case the observed-data nuisance tangent space Λ = Λη . • Observed-data estimating equations, when coarsening is by design, are motivated by considering elements in the space Λ⊥ η , where Λ⊥ η =

and Λ2 =

I(C = ∞)ΛF ⊥ ⊕ Λ2 (∞, Z)

L2 {C, GC (Z)} : E[L2 {C, GC (Z)}|Z] = 0 .

7.6 Exercises for Chapter 7

•

183

The two linear spaces that make up Λ⊥ η are the IPWCC space I(C = ∞)ΛF ⊥ (∞, Z) and the augmentation space Λ2 .

• In order to construct a typical element of Λ2 , for each r = ∞, choose an arbitrary function L2r {Gr (Z)}. Then ⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z)}L2r {Gr (Z)}⎦ − I(C = r)L2r {Gr (Z)} (∞, Z) r=∞

r=∞

is an element of Λ2 .

7.6 Exercises for Chapter 7 Returning to Example 1, introduced in Section 7.1, recall that two blood samples were taken from each individual in a study where one of the objectives was to assess the variability within and between persons in the concentration of some biological marker. As part of the design of this study, a subset of individuals was chosen at random with probability . These individuals had their two blood samples combined and the concentration was obtained on the pooled blood, whereas for the remaining individuals in the study, the concentration was obtained separately for each of the two blood samples. In Section 7.2, we introduced a bivariate normal model for the full data given by (7.11) in terms of parameters (µα , σα2 , σe2 ), and in equation (7.12) we derived the likelihood of the observed coarsened data. The ﬁrst three exercises below relate to this example. 1. Let us ﬁrst consider only the full data for the time being. a) What is the likelihood of the full data (Xi1 , Xi2 ), i = 1, . . . , n? b) Find the MLE for the parameters (µα , σα2 , σe2 ). c) Derive the inﬂuence function of the full-data MLE. 2. Return to the coarsened data whose likelihood for the parameters (µα , σα2 , σe2 ) is given by (7.12). a) Derive the observed data MLE using the coarsened data. b) What is the relative eﬃciency between the coarsened-data MLE and the full-data MLE? Derive this separately for µα , σα2 , and σe2 . 3. We now consider AIPWCC estimators for this problem. a) Derive the augmentation space Λ2 .

184

7 Missing and Coarsening at Random for Semiparametric Models

b) Using the full-data inﬂuence function that was derived in 1(c) above (or, equivalently, using the full-data score vector), write out a set of AIPWCC estimating equations that can be used to obtain observeddata estimators for (µα , σα2 , σe2 ). 4. Derive an estimator for the asymptotic variance of βˆn , the AIPWCC estimator for β for the restricted moment given by the solution to (7.41) where data were missing by design.

8 The Nuisance Tangent Space and Its Orthogonal Complement

8.1 Models for Coarsening and Missingness Two Levels of Missingness In the previous chapter, we gave an example where the missingness (coarsening) mechanism was known to us by design. For most missing-data problems, this is not the case, and we must consider models (either parametric or semiparametric) for the coarsening probabilities. For example, suppose the fulldata Z is a random vector that can be partitioned as Z = (Z1T , Z2T )T , where Z1 is always observed but Z2 may be missing on a subset of individuals. This scenario occurs frequently in practice where, say, one of the variables being collected is missing on some individuals or where a set of variables that are collected at the same time may be missing on some individuals. In this example, there are two levels of missingness; either all the data are available on an individual, which is denoted by letting the complete-case indicator R = 1 (unscripted), or only the data Z1 are available, which is denoted by letting R = 0. Using the coarsening notation, this would correspond to C = ∞ or C = 1, respectively. If we assume that missingness is MAR, then this would imply that P (R = 1|Z) = {1 − P (R = 0|Z)} = {1 − P (R = 0|Z1 )} = π(Z1 ) and P (R = 0|Z1 ) = 1−π(Z1 ). We remind the reader that using the coarsening notation, the probability π(Z1 ) = {∞, G∞ (Z)} and 1 − π(Z1 ) = {1, G1 (Z)}, where G1 (Z) = Z1 and G∞ (Z) = Z. If the missingness was not by design, then the probability of a complete case π(Z1 ), as a function of Z1 , is unknown to us and must be estimated from the data. This is generally accomplished by positing a model in terms of parameters ψ. Since, in this simplest example of missing data, the complete-case indicator is a binary variable, a natural model is the logistic regression model, where π(Z1 , ψ) =

exp(ψ0 + ψ1T Z1 ) , 1 + exp(ψ0 + ψ1T Z1 )

(8.1)

186

8 The Nuisance Tangent Space and Its Orthogonal Complement

and the parameter ψ = (ψ0 , ψ1T )T needs to be estimated from the observed data. Although this illustration assumed a logistic regression model that was linear in Z1 , we could easily have considered more complex models where we include higher-order terms, interactions, regression splines, or whatever else the data analyst deems appropriate. Monotone and Nonmonotone Coarsening for more than Two Levels When there are more than two levels of missingness or coarsening of the data, we distinguish between monotone and nonmonotone coarsening. A form of missingness that often occurs in practice is monotone missingness. Because of its importance, we now describe monotone missingness, or more generally monotone coarsening, in more detail and discuss methods for developing models for such monotone missingness mechanisms. For some problems, we can order the coarsening variable C in such a way that the coarsened data Gr (Z) when C = r is a coarsened version of Gr (Z) for all r > r. In such a case, Gr (Z) is a many-to-one function of Gr+1 (Z); that is, Gr (z) = fr {Gr+1 (z)}, where fr (·) denotes a many-to-one function that depends on r. In other words, G1 (Z) is the most coarsened data, G2 (Z) less so, and G∞ (Z) = Z is not coarsened at all. For example, with longitudinal data, suppose we intend to measure an individual at l diﬀerent time points so that Z = (Y1 , . . . , Yl ), where Yj denotes the measurement at the j-th time point, j = 1, . . . , l. For such longitudinal studies, it is not uncommon for some individuals to drop out during the course of the study, in which case we would observe the data up to the time they dropped out and all subsequent measurements would be missing. This pattern of missingness is monotone and can be described by r 1 2 .. .

Gr (Z) (Y1 ) (Y1 , Y2 )

l − 1 (Y1 , . . . , Yl−1 ) ∞ (Y1 , . . . , Yl ) When data are CAR, we consider models for the coarsening probabilities, which, in general, are denoted by P (C = r|Z = z, ψ) = {r, Gr (z), ψ} in terms of the unknown parameters ψ. However, with monotone coarsening, it is more convenient to consider models for the discrete hazard function, deﬁned as

8.1 Models for Coarsening and Missingness

λr {Gr (Z)} = P (C = r|C ≥ r, Z), r = ∞ = 1, r = ∞.

187

(8.2)

That λr (·) is a function of Gr (Z) follows by noting that the right-hand side of (8.2) equals P (C = r|Z) {r, Gr (Z)} = P (C ≥ r|Z) 1 − r ≤r−1 {r , Gr (Z)}

(8.3)

and by the deﬁnition of monotone coarsening, where Gr (Z) is a function of Gr (Z) for all r < r. We also deﬁne Kr {Gr (Z)} = P (C > r|Z) =

r !

[1 − λr {Gr (Z)}], r = ∞.

(8.4)

r =1

Consequently, we can equivalently express the coarsening probabilities in terms of the discrete hazard functions; namely, {r, Gr (Z)} = Kr−1 {Gr−1 (Z)}λr {Gr (Z)} for r > 1 and λ1 {G1 (Z)} for r = 1.

(8.5)

Equations (8.3), (8.4), and (8.5) demonstrate that there is a one-to-one relationship between coarsening probabilities {r, Gr (Z)} and discrete hazard functions λr {Gr (Z)}. Using discrete hazards, the probability of a complete case (i.e., C = ∞) is given by ! (∞, Z) = 1 − λr {Gr (Z)} . (8.6) r=∞

The use of discrete hazards provides a natural way of thinking about monotone coarsening. For example, suppose we were asked to design a longitudinal study with monotone missingness. We can proceed as follows. First, we would collect G1 (Z) = Y1 . Then, with probability λ1 {G1 (Z)} (that is, with probability depending on Y1 ), we would stop collecting additional data. However, with probability 1 − λ1 {G1 (Z)}, we would collect Y2 , in which case we now have G2 (Z) = (Y1 , Y2 ). If we collected (Y1 , Y2 ), then with probability λ2 {G2 (Z)} we would stop collecting additional data, but with probability 1 − λ2 {G2 (Z)} we would collect Y3 , in which case we would have collected G3 (Z) = (Y1 , Y2 , Y3 ). We continue in this fashion, either stopping at stage r after collecting Gr (Z) = (Y1 , . . . , Yr ) or continuing with probability λr {Gr (Z)} or 1 − λr {Gr (Z)}, respectively. When monotone missingness is viewed in this fashion, it is clear that, conditional on having reached stage r , there are two choices: either stop or continue to the next stage with conditional probability λr {Gr (Z)} or 1 − λr {Gr (Z)}. Therefore, when we build models for the coarsening probabilities of monotone coarsened data, it is natural to consider individual models for each of the discrete hazards. Because

188

8 The Nuisance Tangent Space and Its Orthogonal Complement

of the binary choice made at each stage, logistic regression models for the discrete hazards are often used. For example, for the longitudinal data given above, we may consider a model where it is assumed that λr {Gr (Z)} =

exp(ψ0r + ψ1r Y1 + . . . + ψrr Yr ) , r = ∞. 1 + exp(ψ0r + ψ1r Y1 + . . . + ψrr Yr )

(8.7)

Missing or coarsened data can also come about in a manner that is nonmonotone. For the longitudinal data example given above, suppose patients didn’t necessarily drop out of the study but rather missed visits from time to time. In such a case, some of the longitudinal data might be missing but not necessarily in a monotone fashion. In the worst-case scenario, any of the 2l − 1 combinations of (Y1 , . . . , Yl ) might be missing for diﬀerent patients in the study. Building coherent models for the missingness probabilities for such nonmonotone missing data, even under the assumption that missingness is MAR, is challenging. There have been some suggestions for developing nonmonotone missingness models given by Robins and Gill (1997) using what they call randomized monotone missingness (RMM) models. Because of the complexity of nonmonotone missingness models, we will not discuss such models speciﬁcally in this book. In what follows, we will develop the semiparametric theory assuming that coherent missingness or coarsening models were used. Speciﬁc examples with two levels of missingness or monotone missingness will be used to illustrate the results.

8.2 Estimating the Parameters in the Coarsening Model Models for the coarsening probabilities are described through the parameter ψ. Speciﬁcally, it is assumed that P (C = r|Z = z, ψ) = {r, Gr (z), ψ}, where ψ is often assumed to be a ﬁnite-dimensional parameter. Estimates for the parameter ψ can be obtained using maximum likelihood. We remind the reader that because of the factorization of the observed-data likelihood given by (7.6), the maximum likelihood estimator ψˆn for ψ is obtained by maximizing n !

{Ci , GCi (Zi ), ψ}.

(8.8)

i=1

MLE for ψ with Two Levels of Missingness With two levels of missingness, the likelihood (8.8) simpliﬁes to n ! i=1

{π(Z1i , ψ)}Ri {1 − π(Z1i , ψ)}1−Ri .

(8.9)

8.2 Estimating the Parameters in the Coarsening Model

189

So, for example, if we entertained the logistic regression model (8.1), then the maximum likelihood estimator for (ψ0 , ψ1T )T would be obtained by maximizing (8.9) or, more speciﬁcally, by maximizing n ! exp{(ψ0 + ψ T Z1i )Ri } 1

i=1

1 + exp(ψ0 + ψ1T Z1i )

.

(8.10)

This can be easily implemented in most available statistical software packages. MLE for ψ with Monotone Coarsening Because monotone missingness (or monotone coarsening) is prevalent in many studies, we now consider how to estimate the parameter ψ in this special case. As indicated in Section 8.1, it is more convenient to work with the discrete hazard function to describe monotone coarsening. The discrete hazard, denoted by λr {Gr (Z)}, was deﬁned by (8.2). Therefore, it is natural to consider models for the discrete hazard in terms of parameters ψ, which we denote by λr {Gr (Z), ψ}. An example of such a model for monotone missing longitudinal data was given by (8.7). We also showed in Section 8.1 that there is a oneto-one relationship between the coarsening probabilities {r, Gr (Z)} and the discrete hazard functions. Using (8.5), we see that the coarsening probability can be deduced through the discrete hazards leading to the model {r, Gr (Z), ψ} = λ1 {G1 (Z), ψ} for r = 1, {r, Gr (Z), ψ} =

r−1 !

[1 − λr {Gr (Z), ψ}]λr {Gr (Z), ψ} for r > 1.

(8.11)

r =1

Substituting the right-hand side of (8.11) for (·, ψ) into (8.8) and rearranging terms, we obtain that the likelihood for monotone coarsening can be expressed as I(Ci =r) I(Ci >r) ! ! λr {Gr (Zi ), ψ} 1 − λr {Gr (Zi ), ψ} . (8.12) r=∞ i:Ci ≥r

So, for example, if we consider the logistic regression models used to model the discrete hazards for the monotone missing longitudinal data given by (8.7), then the likelihood is given by l−1 !

! exp(ψ0r + ψ1r Y1i + . . . + ψrr Yri )I(Ci = r) . 1 + exp(ψ0r + ψ1r Y1i + . . . + ψrr Yri )

(8.13)

r=1 i:Ci ≥r

Because the likelihood in (8.13) factors into a product of l−1 logistic regression likelihoods, standard logistic regression software can be used to maximize (8.13).

190

8 The Nuisance Tangent Space and Its Orthogonal Complement

8.3 The Nuisance Tangent Space when Coarsening Probabilities Are Modeled Our ultimate goal is to derive semiparametric estimators for the parameter β of a semiparametric model when the data are coarsened at random. As always, the key to deriving such estimators is to ﬁnd elements orthogonal to the nuisance tangent space that in turn can be used to guide us in constructing estimating equations. Toward that end, we now return to the problem of ﬁnding the nuisance tangent space for semiparametric models with coarsened data when the coarsening probabilities are modeled using additional parameters ψ. We described in the previous sections how such coarsening probability models can be developed and estimated. Therefore, as a starting point, we will assume that such a model for the coarsening probabilities has already been developed as a function of unknown parameters ψ and is denoted by P (C = r|Z) = {r, Gr (Z), ψ}. When the observed data are CAR, we showed in (7.6) that the likelihood can be factored as (r, gr , ψ) pZ (z, β, η)dνZ (z), (8.14) {z:Gr (z)=gr }

where the parameter ψ is ﬁnite-dimensional, say with dimension s. As shown in (7.18), the observed-data nuisance tangent space can be written as a direct sum of two linear subspaces, namely Λ = Λ ψ ⊕ Λη , where Λψ is the space associated with the coarsening model parameter ψ and Λη is the space associated with the inﬁnite-dimensional nuisance parameter η. In Chapter 7, we derived the space Λη and its orthogonal complement. We now consider the space Λψ and some of its properties. Because the space Λψ will play an important role in deriving RAL estimators for β with coarsened data, when the coarsening probabilities are not known and must be modeled, we will denote this space as the coarsening model tangent space and give a formal deﬁnition as follows. Deﬁnition 1. The space Λψ , which we denote as the coarsening model tangent space, is deﬁned as the linear subspace, within H, spanned by the score vector with respect to ψ. That is, Λψ = B q×s Sψs×1 {C, GC (Z), ψ0 } for all B q×s , where Sψs×1 =

∂ log {C, GC (Z), ψ0 } , ∂ψ

and ψ0 denotes the true value of ψ.

(8.15)

8.3 The Nuisance Tangent Space when Coarsening Probabilities Are Modeled

191

One of the properties of the space Λψ is that it is contained in the augmentation space Λ2 , as we now prove. Theorem 8.1. Λψ ⊂ Λ2 Proof. Since {r, Gr (z), ψ} = P (C = r|Z = z, ψ) is a conditional density, then this implies that

{r, Gr (z), ψ} = 1 for all ψ, z.

r

Hence, for a ﬁxed “z,” ∂ {r, Gr (z), ψ} = 0. ∂ψ r Taking the partial derivative inside the sum, dividing and multiplying by {r, Gr (z), ψ}, and setting ψ = ψ0 yields

Sψ {r, Gr (z), ψ0 }{r, Gr (z), ψ0 } = 0

for all z,

r

or E [Sψ {C, GC (Z), ψ0 }|Z] = 0. Hence E B q×s Sψ {C, GC (Z), ψ0 } |Z = 0. * +, typical element of Λψ Consequently, if h{C, GC (Z)} ∈ Λψ , then E[h{C, GC (Z)}|Z] = 0. This implies that Λψ ⊂ Λ2 . Because the parameter ψ and the parameter η separate out in the likelihood (7.6), this should imply that the corresponding spaces Λψ and Λη are orthogonal. We now prove this property more formally. Theorem 8.2. Λψ ⊥ Λη Proof. Recall that the space Λη is given by ( ' ' ( Λη = E αF (Z)|C, GC (Z) for all αF (Z) ∈ ΛF . We ﬁrst demonstrate that Λη ⊥ Λ2 . Choose an arbitrary element h{C, GC (Z)} ∈ Λ2 (i.e., E(h|Z) = 0) and an arbitrary element of Λη , say E{αF (Z)|C, GR (Z)}, for some αF (Z) ∈ ΛF . The inner product of these two elements is

192

8 The Nuisance Tangent Space and Its Orthogonal Complement

' ( E hT {C, GC (Z)}E αF (Z)|C, GC (Z) = E E hT {C, GC (Z)}αF (Z)|C, GC (Z) = E hT {C, GC (Z)}αF (Z) = E E hT {C, GC (Z)}αF (Z)|Z = E E hT {C, GC (Z)}|Z αF (Z) * +,

0 since h ∈ Λ2 = 0. Since Λψ is contained in Λ2 , then this implies that Λψ is orthogonal to Λη . We are now in a position to derive the space orthogonal to the nuisance tangent space.

8.4 The Space Orthogonal to the Nuisance Tangent Space Since inﬂuence functions of RAL estimators for β belong to the space orthogonal to the nuisance tangent space, it is important to derive the space Λ⊥ , where Λ = Λψ ⊕ Λη and Λψ ⊥ Λη . Because the nuisance tangent space Λ is the direct sum of two orthogonal spaces, we can show that the orthogonal complement ⊥ ⊥ ⊥ Λ⊥ = Π(Λ⊥ η |Λψ ) = Π(Λψ |Λη ).

(8.16)

(We leave this as an exercise for the reader.) Using the ﬁrst equality above, a typical element of Λ⊥ can be found by taking an arbitrary element h ∈ Λ⊥ η and computing h − Π(h|Λψ ) = Π(h|Λ⊥ ψ ). In Chapter 7, we showed how to ﬁnd elements orthogonal to Λη . In fact, in Theorem 7.2, we showed the important result that Λ⊥ η can be written as the direct sum of the IPWCC space and the augmentation space. That is, Λ⊥ η =

I(C = ∞)ΛF ⊥ ⊕ Λ2 . (∞, Z)

Speciﬁcally, a typical element of Λ⊥ η is given by formula (7.34); namely, I(C = ∞)ϕ∗F (Z) + L2 {C, GC (Z)} : ϕ∗F (Z) ∈ ΛF ⊥ (∞, Z, ψ0 ) and L2 {C, GC (Z)} ∈ Λ2 .

8.5 Observed-Data Inﬂuence Functions

Therefore, a typical element of Λ⊥ is given by I(C = ∞)ϕ∗F (Z) + L2 {C, GC (Z)} (∞, Z, ψ0 ) I(C = ∞)ϕ∗F (Z) −Π + L2 {C, GC (Z)} Λψ (∞, Z, ψ0 ) ∗F F⊥ for ϕ (Z) ∈ Λ and L2 {C, GC (Z)} ∈ Λ2 .

193

(8.17)

Before discussing how these results can be used to derive RAL estimators for β when data are CAR and when the coarsening probabilities need to be modeled and estimated, which will be deferred to the next chapter, we close this chapter by deﬁning the space of observed-data inﬂuence functions of RAL observed-data estimators for β.

8.5 Observed-Data Inﬂuence Functions Because of condition (i) of Theorem 4.2, observed-data inﬂuence functions ϕ{C, GC (Z)} not only must belong to the space Λ⊥ , but must satisfy E ϕ{C, GC (Z)}SβT {C, GC (Z)} = I q×q , (8.18) where Sβ {C, GC (Z)} is the observed-data score vector with respect to β. For completeness, we will now deﬁne the space of observed-data inﬂuence functions. Theorem 8.3. When data are coarsened at random (CAR) with coarsening probabilities P (C = r|Z) = {r, Gr (Z), ψ}, where Λψ is the space spanned by the score vector Sψ {C, GC (Z)} (i.e., the coarsening model tangent space), then the space of observed-data inﬂuence functions, also denoted by (IF ), is the linear variety contained in H, which consists of elements I(C = ∞)ϕF (Z) ϕ{C, GC (Z)} = + L2 {C, GC (Z)} − Π{[·]|Λψ }, (8.19) (∞, Z, ψ0 ) F where ϕ (Z) is a full-data inﬂuence function and L2 {C, GC (Z)} ∈ Λ2 . Proof. We ﬁrst note that we can use the exact same arguments as used in lemmas 7.1 and 7.2 to show that the observed-data score vector with respect to β is ' ( Sβ {C, GC (Z)} = E SβF (Z)|C, GC (Z) , where SβF (Z) is the full-data score vector with respect to β, SβF (z) =

∂ log pZ (z, β0 , η0 ) . ∂β

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8 The Nuisance Tangent Space and Its Orthogonal Complement

In order for an element of Λ⊥ , given by (8.17), to be an observed-data inﬂuence function, it must satisfy (8.18); that is, I q×q = I(C = ∞)ϕ∗F (Z) E + L2 {C, GC (Z)} (∞, Z) FT − Π[{ · }|Λψ ] E{Sβ (Z)|C, GC (Z)} , which, by the law of iterated conditional expectations, used repeatedly, is equal to T

= E[E{[ · ]SβF (Z)|C, GC (Z)}] T

T

= E{[ · ]SβF (Z)} = E[E{[ · ]SβF (Z)|Z}] T

= E[E{[ · ]|Z}SβF (Z)] I(C = ∞)ϕ∗F (Z) FT =E E + L2 {C, GC (Z)} − Π [{ · }|Λψ ] |Z Sβ (Z) . (∞, Z) Because

E

I(C = ∞)ϕ∗F (Z) Z (∞, Z)

= ϕ∗F (Z),

E [L2 {C, GC (Z)}|Z] = 0 since L2 ∈ Λ2 , and E {Π({ · }|Λψ )|Z} = 0 since Λψ ⊂ Λ2 , this implies that

T E ϕ∗F (Z)SβF (Z) = I q×q .

(8.20)

Equation (8.20) is precisely the condition necessary for a typical element ϕ∗F ∈ ΛF ⊥ to be a full-data inﬂuence function. Therefore, the space of observed-data inﬂuence functions consists of elements I(C = ∞)ϕF (Z) ϕ{C, GC (Z)} = + L2 {C, GC (Z)} − Π{[ · ]|Λψ }, (∞, Z, ψ0 ) F where ϕ (Z) is a full-data inﬂuence function and L2 {C, GC (Z)} ∈ Λ2 . When data are coarsened by design, then the coarsening probabilities {r, Gr (Z)} are known to us. We will sometimes refer to this as the parameter ψ = ψ0 being known. When this is the case, there is no need to introduce the space Λψ . Therefore, we obtain the following simple corollary.

8.6 Recap and Review of Notation

195

Corollary 1. When the coarsening probabilities {r, Gr (Z)} are known to us by design, then the space of observed-data inﬂuence functions is the linear variety consisting of elements I(C = ∞)ϕF (Z) ϕ{C, GC (Z)} = (8.21) + L2 {C, GC (Z)} , (∞, Z, ψ0 ) F where ϕ (Z) is a full-data inﬂuence function and L2 {C, GC (Z)} ∈ Λ2 . Remark 1. Notational convention The space of observed-data inﬂuence functions will be denoted by (IF ) and the space of full-data inﬂuence functions will be denoted by (IF )F . As a result of Corollary 1, when data are coarsened by design, we can write the space (linear variety) of observed-data inﬂuence functions, using shorthand notation, as I(C = ∞)(IF )F (IF ) = (8.22) + Λ2 , (∞, Z) and, by Theorem 8.3, when the coarsening probabilities have to be modeled, as I(C = ∞)(IF )F (IF ) = + Λ2 − Π({ · }|Λψ ). (8.23) (∞, Z)

8.6 Recap and Review of Notation Monotone coarsening •

An important special case of coarsened data is when the coarsening is monotone; that is, the coarsening variable can be ordered in such a way that Gr (Z) is a many-to-one function of Gr+1 (Z) (i.e., Gr (Z) = fr {Gr+1 (Z)}). • When coarsening is monotone, it is convenient to denote coarsening probabilities through the discrete hazard function. The deﬁnition and the relationship to coarsening probabilities are λr {Gr (Z)} = P (C = r|C ≥ r, . Z), r = ∞, r Kr {Gr (Z)} = P (C > r|Z) = r =1 [1 − λr {Gr (Z)}], r = ∞, {r, Gr (Z)} = Kr−1 {Gr−1 (Z)}λr {Gr (Z)}, and the probability of a complete case is P (C = ∞|Z) = (∞, Z) = K {G (Z)}.

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8 The Nuisance Tangent Space and Its Orthogonal Complement

The geometry of semiparametric models with coarsened data •

(IF )F denotes the space of full-data inﬂuence functions where a typical element is denoted by ϕF (Z). This space is a linear variety where (i) ϕF (Z) ∈ ΛF ⊥ , T (ii) E{ϕF (Z)SβF (Z)} = I q×q . • The observed-data nuisance tangent space Λ = Λ ψ ⊕ Λη , Λψ ⊥ Λη , where Λψ , denoted as the coarsening model tangent space, is by the score vector with respect to the parameter ψ used to the coarsening process, and Λη is spanned by the observed-data score vectors for parametric submodels and their mean-square Speciﬁcally, Λη = E{ΛF |C, GC (Z)}.

• Λψ ⊂ Λ 2 , •

Λη ⊥ Λ2 Λ⊥ η

•

•

spanned describe nuisance closures.

=

I(C = ∞)ΛF ⊥ ⊕ Λ2 . (∞, Z)

When the coarsening probabilities are unknown to us and need to be modeled, then I(C = ∞)ΛF ⊥ I(C = ∞)ΛF ⊥ Λ⊥ = . ⊕ Λ2 − Π ⊕ Λ2 Λψ (∞, Z) (∞, Z) (IF ) = space of observed-data inﬂuence functions ϕ{C, GC (Z)} . When the coarsening probabilities are unknown to us and need to be modeled, then I(C = ∞)(IF )F I(C = ∞)(IF )F (IF ) = + Λ2 − Π + Λ2 Λ ψ . (∞, Z) (∞, Z) When the coarsening probabilities are known to us by design, then (IF ) =

I(C = ∞)(IF )F + Λ2 . (∞, Z)

8.7 Exercises for Chapter 8 1. In Section 8.1, we described how data may be monotonically missing using longitudinal data (Y1 , . . . , Yl ) as an illustration. We also described a

8.7 Exercises for Chapter 8

197

model for the coarsening process where we modeled the discrete hazard function using equation (8.7) and derived the likelihood contribution for the coarsening model in (8.13). Let ψr denote the vector of parameters (ψ0r , . . . , ψrr )T for r = 1, . . . , l − 1, and let ψ denote the entire parameter T space for the coarsening probabilities; that is, ψ = (ψ1T , . . . , ψl−1 )T . a) Derive the score vector Sψr {C, GC (Z)} =

∂ log[{C, GC (Z), ψ}] , r = 1, . . . , l − 1. ∂ψr

b) Show that the coarsening model tangent space Λψ is equal to the direct sum Λψ1 ⊕ . . . ⊕ Λψl−1 , where Λψr is the linear space spanned by the vector Sψr {C, GC (Z)}, r = 1, . . . , l − 1, and that these spaces are mutually orthogonal to each other. 2. Give a formal proof of (8.16).

9 Augmented Inverse Probability Weighted Complete-Case Estimators

9.1 Deriving Semiparametric Estimators for β ψ known We begin by assuming the parameter ψ0 that deﬁnes the coarsening model is known to us by design. Semiparametric estimators for β can be obtained by deriving elements orthogonal to the nuisance tangent space and using these to motivate estimating functions that can be used to construct estimating equations whose solution will lead to semiparametric estimators for β. In Chapter 7, we showed that when the parameter ψ0 is known, the space orthogonal to the nuisance tangent space is the direct sum of the IPWCC space and the augmentation space, namely Λ⊥ =

I(C = ∞)ΛF ⊥ ⊕ Λ2 , (∞, Z, ψ0 )

where a typical element of this space is I(C = ∞)ϕ∗F (Z) + L2 {C, GC (Z), ψ0 }, (∞, Z, ψ0 )

(9.1)

where ϕ∗F (Z) is an arbitrary element orthogonal to the full-data nuisance tangent space (i.e., ϕ∗F (Z) ∈ ΛF ⊥ ), and L2 {C, GC (Z), ψ0 } is an arbitrary element of Λ2 that can be constructed by taking arbitrary functions L2r {Gr (Z)}, r = ∞, and then using (7.37). In Section 7.4, we gave examples of how these results could be used to derive augmented inverse probability weighted complete-case (AIPWCC) estimators for the regression parameters in a restricted moment model with missing data by design when there were two levels of missingness. We now expand this discussion to more general coarsening by design mechanisms. If we want to obtain a semiparametric estimator for β, we would proceed as follows. We start with a full-data estimating equation that yields a fulldata RAL estimator for β. It will be assumed that we know how to construct

200

9 Augmented Inverse Probability Weighted Complete-Case Estimators

such estimating equations for full-data semiparametric models. For example, a full-data m-estimator could be derived by solving the estimating equation n

m(Zi , β) = 0,

i=1

where the estimating function evaluated at the truth, m(Z, β0 ), was chosen so that m(Z, β0 ) = ϕ∗F (Z) ∈ ΛF ⊥ . For example, we take m(Z, β) = A(X){Y − µ(X, β)} for the restricted moment model. The inﬂuence function of such a full-data estimator for β was derived in Chapter 3, formula (3.6), and is given by −1 ∂m(Zi , β0 ) − E m(Zi , β0 ) = ϕF (Zi ). (9.2) ∂β T Remark 1. An estimating function m(Z, β) is a function of the random variable Z and the parameter β being estimated, and the corresponding mestimator is the nsolution to the estimating equation made up of a sum of iid quantities i=1 m(Zi , β) = 0. However, in many cases, elements orthogonal to the nuisance tangent are deﬁned as ϕ∗F (Z) = m(Z, β0 , η0 ), where η0 denotes the true value of the full-data nuisance parameter. Consequently, it may not be possible to deﬁne an estimating function m∗ (Z, β) that is only a function of Z and β that satisﬁes Eβ,η {m∗ (Z, β)} = 0 for all β and η, as would be necessary to obtain consistent asymptotically normal estimators for all β and η. However, if we could ﬁnd a consistent estimator for η, say ηˆn , then a natural strategy would be to derive an estimator for β that is the solution to the estimating equation n

m(Zi , β, ηˆn ) = 0.

(9.3)

i=1

The estimating equation (9.3) is not a sum of iid quantities and hence the resulting estimator is not, strictly speaking, an m-estimator. However, in many situations, and certainly in all cases considered in this book, the estimator βˆn that solves (9.3) will be asymptotically equivalent to the m-estimator βˆn∗ that solves the equation n m(Zi , β, η0 ) = 0, i=1 P with η0 known, in the sense that n1/2 (βˆn −βˆn∗ ) − → 0. Without going into detail, this asymptotic equivalence occurs because m(Z, β0 , η0 ) = ϕ∗F (Z) is orthogonal to the nuisance tangent space. We illustrated this asymptotic equivalence for parametric models in Section 3.3 using equation (3.30) (also see Remark 4 of this section). Therefore, from here on, with a slight abuse of notation, we will still refer to estimators such as those that solve (9.3) as m-estimators with estimating function m(Z, β).

9.1 Deriving Semiparametric Estimators for β

201

With coarsened data by design, we use (9.1) to motivate the following estimating equation: n I(Ci = ∞)m(Zi , β) + L2 {Ci , GCi (Zi ), ψ0 } = 0. (9.4) (∞, Zi , ψ0 ) i=1 The estimator that is the solution to the estimating equation (9.4) is referred to as an AIPWCC estimator. If we take the element L2 (·) to be identically equal to zero, then the estimating equation becomes n I(Ci = ∞)m(Zi , β) = 0, (∞, Zi , ψ0 ) i=1 and the resulting estimator is an IPWCC estimator since only complete cases are considered in the sum above (i.e., {i : Ci = ∞}), weighted by the inverse probability of being a complete case. The term L2 {Ci , GCi (Zi ), ψ0 } allows contributions to the sum by observations that are not complete (i.e., coarsened), and this is referred to as the augmented term. Using standard Taylor series expansions for m-estimators (which we leave as an exercise for the reader), we can show that the inﬂuence function of the estimator, derived by solving (9.4), is equal to −1 ∂m(Zi , β0 ) I(Ci = ∞)m(Zi , β0 ) − E {C , G (Z ), ψ } + L 2 i C i 0 i ∂β T (∞, Zi , ψ0 ) =

I(Ci = ∞)ϕF (Zi ) + L∗2 {Ci , GCi (Zi ), ψ0 }, (∞, Zi , ψ0 )

where ϕF (Zi ) was deﬁned in (9.2) and −1 ∂m(Zi , β0 ) L∗2 = − E L2 ∈ Λ2 . ∂β T

(9.5)

(9.6)

Therefore, we can now summarize these results. If coarsening of the data were by design with known coarsening probabilities {r, Gr (Z), ψ0 }, for all r, and we wanted to obtain an observed-data RAL estimator for β in a semiparametric model, we would proceed as follows. 1. Choose a full-data estimating function m(Z, β). 2. Choose an element of the augmentation space Λ2 as follows. a) For each r = ∞, choose a function L2r {Gr (Z)}. b) Construct L2 {C, GC (Z), ψ0 } to equal ⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z), ψ0 }L2r {Gr (Z)}⎦ (∞, Z, ψ0 ) r=∞ − I(C = r)L2r {Gr (Z)}. r=∞

202

9 Augmented Inverse Probability Weighted Complete-Case Estimators

3. Obtain the estimator for β by solving equation (9.4). The resulting estimator, βˆn , under suitable regularity conditions, will be a consistent, asymptotically normal RAL estimator for β with inﬂuence function given by (9.5). The asymptotic variance of this estimator is, of course, the variance of the inﬂuence function. Estimators for the asymptotic variance can be obtained using the sandwich variance estimator (3.10) derived in Chapter 3 speciﬁcally for m-estimators. We see from this construction that the estimator depends on the choice of m(Z, β) and the functions L2r {Gr (Z)}, r = ∞. Of course, we would want to choose these functions so that the resulting estimator is as eﬃcient as possible. This issue will be the focus of Chapters 10 and 11. ψ unknown The development above shows how we can take results regarding semiparametric estimators for the parameter β for full-data models and modify them to estimate the parameter β with coarsened data (CAR) when the the coarsening probabilities are known to us by design. In most problems, the coarsening probabilities are not known and must be modeled using the unknown parameter ψ. We discussed models for the coarsening process and estimators for the parameters in these models in Chapter 8. We also showed in Chapter 8 the impact that such models have on the observed-data nuisance tangent space, its orthogonal complement, and the space of observed-data inﬂuence functions. If the parameter ψ is unknown, two issues emerge: (i) The unknown parameter ψ must be estimated. (ii) The inﬂuence function of an observed-data RAL estimator for β must be an element in the space deﬁned by (7.37) (i.e., involving a projection onto the coarsening model tangent space Λψ ). One obvious strategy for estimating β with coarsened data when ψ is unknown is to ﬁnd a consistent estimator for ψ and substitute this estimator for ψ0 in the estimating equation (9.4). A natural estimator for ψ is obtained by maximizing the coarsening model likelihood (8.8). The resulting MLE is denoted by ψˆn . The inﬂuence function of the estimator for β, obtained by substituting the maximum likelihood estimator ψˆn for ψ0 in equation (9.4), is given by the following important theorem. Theorem 9.1. If the coarsening process follows a parametric model, and if ψ is estimated using the maximum likelihood estimator, say ψˆn , or any eﬃcient estimator of ψ, then the solution to the estimating equation # $ n I(Ci = ∞)m(Zi , β) (9.7) + L2 {Ci , GCi (Zi ), ψˆn } = 0 ˆn ) (∞, Z , ψ i i=1 will be an estimator whose inﬂuence function is

9.1 Deriving Semiparametric Estimators for β

I(Ci = ∞)ϕF (Zi ) + L∗2 {Ci , GCi (Zi ), ψ0 } (∞, Zi , ψ0 ) I(Ci = ∞)ϕF (Zi ) −Π + L∗2 {Ci , GCi (Zi ), ψ0 } Λψ , (∞, Zi , ψ0 )

203

(9.8)

where ϕF (·) and L∗2 (·) are deﬁned by (9.2) and (9.6). We note that such an inﬂuence function is indeed a member of the class of observed-data inﬂuence functions given by (8.19). For notational convenience, we denote a typical inﬂuence function, if the parameter ψ is known, by ϕ{C ˜ i , GCi (Zi ), ψ} =

I(Ci = ∞)ϕF (Zi ) + L∗2 {Ci , GCi (Zi ), ψ}, (∞, Zi , ψ)

(9.9)

and a typical inﬂuence function, if ψ is unknown, by ϕ{Ci , GCi (Zi ), ψ} = ϕ{C ˜ i , GCi (Zi ), ψ} − Π[ϕ{C ˜ i , GCi (Zi ), ψ}|Λψ ]. Before giving the proof of the theorem above we present the following lemma. Lemma 9.1. ∂ ϕ{C, ˜ GC (Z), ψ0 } T E = −E ϕ{C, ˜ G (Z), ψ }S {C, G (Z), ψ } . C 0 C 0 ψ ∂ψ T

(9.10)

Proof. Lemma 9.1 We ﬁrst note that the conditional expectation E[h{C, GC (Z)}|Z] for a typical function h, as given by (7.35), only depends on the parameter ψ. Namely, this conditional expectation equals hr {Gr (Z)}{r, Gr (Z), ψ}. (9.11) Eψ (h|Z) = r

Because of the deﬁnition of ϕ{C, ˜ GC (Z), ψ} given by (9.9) and the fact that L∗2 {C, GC (Z), ψ} ∈ Λ2 , we obtain for any ψ, that ' ( ˜ GC , ψ) Z = ϕF (Z), Eψ ϕ{C, which, by equation (9.11), equals ϕ{r, ˜ Gr (z), ψ}{r, Gr (z), ψ} = ϕF (z) for all z, ψ, r

where ϕF (z) does not include the parameter ψ. Therefore ∂ ϕ{r, ˜ Gr (z), ψ}{r, Gr (z), ψ} = 0 ∂ψ T r

for all z, ψ.

(9.12)

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9 Augmented Inverse Probability Weighted Complete-Case Estimators

Diﬀerentiating the product inside the sum (9.12) and setting ψ = ψ0 yields ∂ ϕ{r, ˜ Gr (z), ψ0 } ∂ψ T

r

+

{r, Gr (z), ψ0 }

ϕ{r, ˜ Gr (z), ψ0 }

r

∂{r, Gr (z), ψ0 }/∂ψ T {r, Gr (z), ψ0 } = 0, {r, Gr (z), ψ0 }

or E

∂ ϕ{C, ˜ GC (Z), ψ0 } T Z = −E ϕ{C, ˜ G (Z), ψ }S {C, G (Z), ψ }|Z , C 0 C 0 ψ ∂ψ T

which, after taking unconditional expectations, implies ∂ ϕ{C, ˜ GC (Z), ψ0 } T E = −E ϕ{C, ˜ GC (Z), ψ0 }Sψ {C, GC (Z), ψ0 } . ∂ψ T We are now in a position to prove Theorem 9.1. Proof. Theorem 9.1 The usual expansion of (9.7) about β0 , but keeping ψˆn ﬁxed, yields n1/2 (βˆn − β0 ) = # $ n I(Ci = ∞)ϕF (Zi ) −1/2 ∗ n + L2 {Ci , GCi (Zi ), ψˆn } + op (1), (∞, Zi , ψˆn ) i=1

(9.13)

where ϕF (Zi ) is given by (9.2) and L∗2 {Ci , GCi (Zi ), ψ} is given by (9.6). Now we expand ψˆn about ψ0 to obtain n1/2 (βˆn − β0 ) =n−1/2 # + n

n

ϕ{C ˜ i , GCi (Zi ), ψ0 }

i=1 −1

$ n ∂ ϕ{C ˜ i , GCi (Zi ), ψn∗ } 1/2 ˆ n (ψn − ψ0 ) + op (1), ∂ψ T i=1 (9.14)

where ψn∗ is some intermediate value between ψˆn and ψ0 . Since under usual regularity conditions ψn∗ converges in probability to ψ0 , we obtain n−1

n ∂ ϕ{C ˜ i , GCi (Zi ), ψn∗ } P ∂ ϕ{C ˜ i , GCi (Zi ), ψ0 } . − → E ∂ψ T ∂ψ T i=1

(9.15)

Standard results for ﬁnite-dimensional parametric models, as derived in Chapter 3, can be used to show that the inﬂuence function of the MLE ψˆn is given by

9.1 Deriving Semiparametric Estimators for β

E Sψ {C, GC (Z), ψ0 }SψT {C, GC (Z), ψ0 }

205

−1 Sψ {C, GC (Z), ψ0 },

(9.16)

where Sψ {C, GC (Z), ψ0 } is the score vector with respect to ψ deﬁned in (8.15). The inﬂuence function of ψˆn given by (9.16), together with (9.15) and (9.10) of Lemma 9.1, can be used to deduce that (9.14) is equal to n1/2 (βˆn − β0 ) n −1/2 ϕ{C ˜ i , GCi (Zi ), ψ0 } − E ϕ{C ˜ i , GCi (Zi ), ψ0 }SψT {Ci , GCi , ψ0 } =n i=1

−1 E Sψ {Ci , GCi (Zi ), ψ0 }SψT {Ci , GCi (Zi ), ψ0 } Sψ {Ci , GCi (Zi ), ψ0 } + op (1).

(9.17)

The space Λψ is the linear space spanned by Sψ {C, GC (Z), ψ0 }. Therefore, using results from Chapter 2 (equation (2.4)) for ﬁnding projections onto ﬁnite-dimensional linear subspaces, we obtain that −1 ˜ ψT ) E(Sψ SψT ) Sψ {C, GC (Z)}. Π [ϕ{C, ˜ GC (Z)}|Λψ ] = E(ϕS

(9.18)

Consequently, as a result of (9.17) and (9.18), the inﬂuence function of βˆn is given by ˜ GC (Z), ψ0 }|Λψ ], ϕ{C, ˜ GC (Z), ψ0 } − Π[ϕ{C, where ϕ{C, ˜ GC (Z), ψ0 } =

I(C = ∞)ϕF (Z) + L∗2 {C, GC (Z), ψ0 }. (∞, Z, ψ0 )

Theorem 9.1 is important because it gives us a prescription for deriving observed-data RAL estimators for β when coarsening probabilities need to be modeled and estimated. Summarizing these results, if the data are coarsened at random with coarsening probabilities that need to be modeled, and we want to obtain an observed-data RAL estimator for β in a semiparametric model, we would proceed as follows. 1. Choose a model for the coarsening probabilities in terms of a parameter ψ, namely {r, Gr (Z), ψ}, for all r. In Section 8.1, we discussed how such models can be derived when there are two levels of coarsening or when the coarsening is monotone. 2. Using the observed data, estimate the parameter ψ using maximum likelihood (that is, by maximizing (8.8)), and denote the estimator by ψˆn . 3. Choose a full-data estimating function m(Z, β). 4. Choose an element of the augmentation space Λ2 by a) for each r = ∞, choosing a function L2r {Gr (Z)} and

206

9 Augmented Inverse Probability Weighted Complete-Case Estimators

b) constructing L2 {C, GC (Z), ψˆn } to equal ⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z), ψˆn }L2r {Gr (Z)}⎦ (∞, Z, ψˆn ) r=∞ − I(C = r)L2r {Gr (Z)}. r=∞

5. Obtain the estimator for β by solving equation (9.7). The resulting estimator, βˆn , under suitable regularity conditions, will be a consistent, asymptotically normal RAL estimator for β with inﬂuence function given by (9.8). Interesting Fact If the parameter ψ0 was known to us and we used equation (9.4) to derive an estimator for β, then, since this estimator is asymptotically linear, the asymptotic variance would be the variance of the inﬂuence function (9.5), namely I(C = ∞)ϕF (Z) var + L∗2 {C, GC (Z), ψ0 } . (∞, Z, ψ0 ) If, however, ψ was estimated using the MLE ψˆn and equation (9.7) was used to derive an estimator for β, then the asymptotic variance would be the variance of its inﬂuence function namely I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) var + L∗2 {C, GC (Z), ψ0 } − Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) + L∗2 {C, GR (Z), ψ0 } Λψ . By the Pythagorean theorem, the variance of the second estimator must be less than or equal to the variance of the ﬁrst. This leads to the interesting and perhaps unintuitive result that, even if we know the coarsening probabilities (say we coarsen the data by design), then we would still obtain a more eﬃcient estimator for β by estimating the parameter ψ in a model that contains the truth and substituting this estimator for ψ in equation (9.4) rather than using the true ψ0 itself. Estimating the Asymptotic Variance The asymptotic variance of the RAL estimator βˆn is the variance of the inﬂuence function (9.8), which we denote by Σ. An estimator for the asymptotic ˆ n , can be obtained using a sandwich variance estimator. For comvariance, Σ pleteness, we now describe how to construct this estimator:

9.2 Additional Results Regarding Monotone Coarsening

207

" −1 ∂m(Z, βˆn ) ˆ ˆ Σn = E ∂β T n × n−1 g{Ci , GCi (Zi ), ψˆn , βˆn }g T {Ci , GCi (Zi ), ψˆn , βˆn } i=1

" T −1 ∂m(Z, βˆn ) ˆ × E , ∂β T

(9.19)

where ˆ E

∂m(Z, βˆn ) ∂β T

" =n

−1

n i=1

I(Ci = ∞)∂m(Zi , βˆn )/∂β T (∞, Zi , ψˆn )

" ,

g{Ci , GCi (Zi ), ψˆn , βˆn } = T T −1 ˆ ˆ q{Ci , GCi (Zi ), ψˆn , βˆn } − E(qS Sψ {Ci , GCi (Zi ), ψˆn }, ψ ){E(Sψ Sψ )} q{Ci , GCi (Zi ), ψˆn , βˆn } = T −1 ˆ E(qS ψ) = n

n

I(Ci = ∞)m(Zi , βˆn ) + L2 {Ci , GCi (Zi ), ψˆn }, (∞, Zi , ψˆn )

q{Ci , GCi (Zi ), ψˆn , βˆn }SψT {Ci , GCi (Zi ), ψˆn },

i=1

and ˆ ψ S T ) = n−1 E(S ψ

n

Sψ {Ci , GCi (Zi ), ψˆn }SψT {Ci , GCi (Zi ), ψˆn }.

i=1

9.2 Additional Results Regarding Monotone Coarsening The Augmentation Space Λ2 with Monotone Coarsening In deriving arbitrary semiparametric estimators for β with coarsened data, whether the coarsening process was by design, ψ0 known, or whether the parameter ψ describing the coarsening process had to be estimated, a key component of the estimating equation, either (9.4) or (9.7), was the augmentation term L2 {C, GC (Z)} ∈ Λ2 . In (7.37), we gave a general representation for such an arbitrary element of Λ2 when the data are coarsened at random. However, in the special case when we have monotone coarsening, it will be convenient to derive another equivalent representation for the elements in Λ2 . This representation uses discrete hazards as deﬁned in (8.2) and is given in the following theorem.

208

9 Augmented Inverse Probability Weighted Complete-Case Estimators

Theorem 9.2. Under monotone coarsening, a typical element of Λ2 can be written as I(C = r) − λr {Gr (Z)}I(C ≥ r) Lr {Gr (Z)}, (9.20) Kr {Gr (Z)} r=∞

where λr {Gr (Z)} and Kr {Gr (Z)} are deﬁned by (8.2) and (8.4), respectively, and Lr {Gr (Z)} denotes an arbitrary function of Gr (Z) for r = ∞. Remark 2. Equation (9.20) is made up of a sum of mean-zero conditionally uncorrelated terms; i.e., it has a martingale structure. We will take advantage of this structure in the next chapter when we derive the more eﬃcient double robust estimators. Proof. Using (7.37), we note that a typical element of Λ2 can be written as I(C = ∞){r, Gr (Z)} L2r {Gr (Z)}, I(C = r) − (9.21) (∞, Z) r=∞

where L2r {Gr (Z)} denotes an arbitrary function of Gr (Z) for r = ∞. To simplify notation, let λr = λr {Gr (Z)}, Kr = Kr {Gr (Z)}, Lr = Lr {Gr (Z)}, and L2r = L2r {Gr (Z)}. We will prove that there is a one-to-one relationship between the elements in (9.20) and the elements of (9.21), speciﬁcally that L2r =

1 Kr−1

Lr −

r−1 λj Lj for all r = ∞, K j j=1

(9.22)

and conversely Lr = Kr−1 L2r +

r−1

j L2j for all r = ∞.

(9.23)

j=1

We prove (9.23) by induction. For r = 1, notice that K0 = 1, and hence by (9.22), L1 = L21 . Now suppose that (9.23) holds for any i ≤ r. Then for r + 1, we have from (9.22) that L2(r+1) =

r λi 1 Lr+1 − Li . Kr K i i=1

Therefore, Lr+1 = Kr L2(r+1) +

r Kr λi i=1

= Kr L2(r+1) +

Li

r Kr λi i=1

= Kr L2(r+1) +

Ki

Ki

r K r i i=1

Ki

⎛ ⎝Ki−1 L2i +

i−1

⎞ j L2j ⎠

j=1

L2i +

r i−1 K r λ i j i=1 j=1

Ki

L2j .

9.2 Additional Results Regarding Monotone Coarsening

Interchange the order of summation, Lr+1 = Kr L2(r+1) +

r K r i i=1

= Kr L2(r+1) +

r

Ki

⎛

Kr j L2j ⎝

j=1

Note that

r−1

L2i +

Kr j L2j

j=1

r λi K i i=j+1 ⎞

r 1 λi ⎠ . + Kj i=j+1 Ki

1 λj+1 1 − λj+1 λj+1 1 + = + = , Kj Kj+1 Kj+1 Kj+1 Kj+1

and hence

r λi 1 1 + = . Kj i=j+1 Ki Kr

Therefore, Lr+1 = Kr L2(r+1) +

r

j L2j .

j=1

Substituting (9.23) into (9.20) yields ⎛ ⎞ r−1 I(C = r) − λr I(C ≥ r) ⎝Kr−1 L2r + j L2j ⎠ Kr j=1

r=∞

=

I(C = r) − λr I(C ≥ r) Kr−1 L2r Kr

r=∞

+

j L2j

j=∞

j+1≤r=∞

I(C = r) − λr I(C ≥ r) Kr

I(C = r) − λr I(C ≥ r) = Kr−1 L2r Kr r=∞

+

r=∞

r L2r

r+1≤j=∞

I(C = j) − λj I(C ≥ j) . Kj

But since I(C = j) = I(C ≥ j) − I(C ≥ j + 1), we have that

I(C = j) − λj I(C ≥ j) Kj r+1≤j=∞ I(C ≥ j) I(C ≥ j + 1) = − Kj−1 Kj r+1≤j=∞

I(C ≥ r + 1) I(C = ∞) = − . Kr ∞

209

210

9 Augmented Inverse Probability Weighted Complete-Case Estimators

Therefore, (9.20) can be written as I(C = r)Kr−1 I(C ≥ r)r I(C ≥ r + 1)r I(C = ∞)r L2r − + − Kr Kr Kr ∞ r=∞ I(C = r)Kr−1 I(C = r)r I(C = ∞)r L2r = − − Kr Kr ∞ r=∞ I(C = ∞)r I(C = r) − L2r , = ∞ r=∞

which is exactly (9.21). Similarly, substituting (9.22) into (9.21) yields (9.20). Example 1. Longitudinal data with monotone missingness Consider the following problem. A promising new drug for patients with HIV disease is to be evaluated against a control treatment in a randomized clinical trial. A sample of n patients with HIV disease are randomized with equal probability (.5) to receive either the new treatment (X = 1) or the control treatment (X = 0). The primary endpoint used to evaluate the treatments is change in CD4 counts measured over time. Speciﬁcally, CD4 counts are to be measured at time points t1 < . . . < tl , where t1 = 0 denotes the time of entry into the study when a baseline CD4 count measurement is taken and t2 < . . . < tl are the subsequent times after treatment when CD4 counts will be measured. For example, CD4 count measurements may be taken every six months, with a ﬁnal measurement at two years. The data for individual i can be summarized as Zi = (YiT , Xi )T , where Yi = (Y1i , . . . , Yli )T , with Yji denoting the CD4 count measurement for patient i at time tj and Xi denoting the treatment indicator to which patient i was assigned. Suppose that it is generally believed that, after treatment is initiated, CD4 counts will roughly follow a linear trajectory over time. Therefore, a linear model E(Yil×1 |Xi ) = H l×3 (Xi )β 3×1 , (9.24) is used to describe the data, where the design matrix H(Xi ) is an l × 3 matrix with elements Hjj (Xi ), j = 1, . . . , l, j = 1, 2, 3, and Hj1 (Xi ) = 1, Hj2 (Xi ) = tj , and Hj3 (Xi ) = Xi tj . This model implies that the mean CD4 count at time tj is given by E(Yji |Xi ) = β1 + β2 tj + β3 Xi tj . Hence, if Xi = 0, then the expected CD4 count is β1 +β2 tj , whereas if Xi = 1, then the expected CD4 count is β1 + (β2 + β3 )tj . This reﬂects the belief that CD4 response follows a linear trajectory after treatment is initiated. Because of randomization, the mean baseline response at time t1 = 0 equals β1 , the same for both treatments, but the slope of the trajectory may depend on

9.2 Additional Results Regarding Monotone Coarsening

211

treatment. The parameter β3 reﬂects the strength of the treatment eﬀect, where β3 = 0 corresponds to the null hypothesis of no treatment eﬀect. The model (9.24) is an example of a restricted moment model, where E(Y |X) = µ(X, β) = H(X)β. If the data were collected on everyone (i.e., full data), then according to the results developed in Section 4.5, a full-data estimating function would in general be based on A3×l (X){Y − µ(X, β)}, and the optimal estimating function would be DT (X)V −1 (X){Y − µ(X, β)}, where D(X) = ∂µ(X,β) and V (X) = var(Y |X). In this example, D(X) = ∂β T H(X). Although we would expect the longitudinal CD4 count measurements to be correlated, for simplicity we use a working variance function V (X) = σ 2 I l×l . This leads to the full-data estimating function m(Z, β) = H T (X){Y − H(X)β}, and the corresponding full-data estimator would be obtained by solving the estimating equation n

H T (Xi ){Yi − H(Xi )β} = 0.

i=1

In this study, however, some patients dropped out during the course of the study, in which case we would observe the data up to the time they dropped out but all subsequent CD4 count measurements would be missing. This is an example of monotone coarsening as described in Section 8.1, where there are = l − 1 levels of coarsening and where Gr (Zi ) = (Xi , Y1i , . . . , Yri )T , r = 1, . . . , l − 1 and G∞ (Zi ) = (Xi , Y1i , . . . , Yli )T . Because dropout was not by design, we need to model the coarsening probabilities in order to derive AIPWCC estimators for β. Since the data are monotonically coarsened, it is more convenient to model the discrete hazard function. Assuming the coarsening is CAR, we consider a series of logistic regression models similar to (8.7), namely λr {Gr (Z)} =

exp(ψ0r + ψ1r Y1 + . . . + ψrr Yr + ψ(r+1)r X) . 1 + exp(ψ0r + ψ1r Y1 + . . . + ψrr Yr + ψ(r+1)r X)

(9.25)

Note 1. The only diﬀerence between equations (8.7) and (9.25) is the inclusion of the treatment indicator X. Therefore, the parameter ψ in this example is ψ = (ψ0r , . . . , ψ(r+1)r , r = 1, . . . , l − 1)T . The MLE ψˆn is obtained by maximizing the likelihood (8.13). We now have most of the components necessary to construct an AIPWCC estimator for β. We still need to deﬁne an element of the augmentation space Λ2 . In accordance with Theorem 9.2, for monotonically coarsened data, we must choose a function Lr {Gr (Z)}, r = 1, . . . , l − 1 (that is, a function Lr (X, Y1 , . . . , Yr )) and then use (9.20) to construct an element L2 {C, GC (Z)} ∈ Λ2 . Putting all these diﬀerent elements together, we can derive an observeddata RAL estimator for β by using the results of Theorem 9.1, equation (9.7), by solving the estimating equation

212

9 Augmented Inverse Probability Weighted Complete-Case Estimators n I(Ci = ∞)H T (Xi ){Yi − H(Xi )β} (9.26) (∞, Zi , ψˆn ) i=1 " l−1 I(Ci = r) − λr {Gr (Zi ), ψˆn }I(Ci ≥ r) Lr {Gr (Z)} = 0, + Kr {Gr (Zi ), ψˆn } r=1

where λr {Gr (Zi ), ψˆn } =

exp(ψˆ0r + ψˆ1r Y1i + . . . + ψˆrr Yri + ψˆ(r+1)r Xi ) , 1 + exp(ψˆ0r + ψˆ1r Y1i + . . . + ψˆrr Yri + ψˆ(r+1)r Xi )

Kr {Gr (Zi ), ψˆn } = r !

1 , ˆ ˆ 1 + exp(ψ0r + ψ1r Y1i + . . . + ψˆr r Yr i + ψˆ(r +1)r Xi ) r =1

and (∞, Zi , ψˆn ) = Kl−1 {Gl−1 (Zi ), ψˆn }. The estimator βˆn , the solution to (9.26), will be an observed-data RAL estimator for β that is consistent and asymptotically normal, assuming, of course, that the model for the discrete hazard functions were correctly speciﬁed. An estimator for the asymptotic variance of the estimator for βˆn can be obtained by using the sandwich variance estimator given by (9.19). The eﬃciency of the estimator will depend on the choice for m(Zi , β) and the choice for Lr (Xi , Y1i , . . . , Yri ), r = 1, . . . , l − 1. For illustration, we chose m(Zi , β) = H T (Xi ){Yi − H(Xi )β}, but this may or may not be a good choice. Also, we did not discuss choices for Lr (Xi , Y1i , . . . , Yri ), r = 1, . . . , l − 1. If Lr (·) were set equal to zero, then the corresponding estimator would be the IPWCC estimator. A more detailed discussion on choices for these functions and how they aﬀect the eﬃciency of the resulting estimator will be given in Chapters 10 and 11. Since we are on the topic of monotone coarsening, we take this opportunity to note that in survival analysis the survival data are often right censored. The notion of right censoring was introduced in Section 5.2, where we derived semiparametric estimators for the proportional hazards model. Right censoring can be viewed as a speciﬁc example of monotone coarsening. That is, if data in a survival analysis are right censored, then we don’t observe any data subsequent to the censoring time. The main diﬀerence between right censoring for survival data and the monotone coarsening presented thus far is that the censoring time for survival data is continuous, taking on an uncountably inﬁnite number of values, whereas we have only considered coarsening models with a ﬁnite number of coarsened conﬁgurations. Nonetheless, we can make the analogy from monotone coarsening to censoring in survival analysis by

9.3 Censoring and Its Relationship to Monotone Coarsening

213

considering continuous-time hazard rates instead of discrete hazard probabilities. In the next section, we give some of the analogous results for censored data. We show how to cast a censored-data problem as a monotone coarsening problem and we derive a typical inﬂuence function of an observed-data (censored data) inﬂuence function for a parameter β in terms of the inﬂuence function of a full-data (uncensored) estimator for β. Much of the exposition that follows is motivated by the work in the landmark paper of Robins and Rotnitzky (1992). To follow the results in the next section, the reader must be familiar with counting processes and the corresponding martingale processes used in an advanced course in censored survival analysis. If the reader does not have this background, then this section can be skipped without having it aﬀect the reading of the remainder of the book.

9.3 Censoring and Its Relationship to Monotone Coarsening In survival analysis, full data for a single individual can be summarized as ¯ )}, where T denotes the survival time, X(u) denotes the value of {T, X(T ¯ ) is the covariates (possibly time-dependent) measured at time u, and X(T history of time-dependent covariates X(u), u ≤ T . As always, the primary focus is to estimate parameters β that characterize important aspects of the ¯ )}. We will assume that we know how to ﬁnd estimators distribution of {T, X(T ¯ i (Ti )}, i = 1, . . . , n. for the parameter of interest if we had full data {Ti , X Example 2. As an example, consider the following problem. Suppose we are interested in estimating the mean medical costs for patients with some illness during the duration of their illness. For patient i in our sample, let Ti denote the duration of their illness and let Xi (u) denote the accumulated hospital costs incurred for patient i at time u (measured as the time from the beginning of illness). Clearly, Xi (u) is a nondecreasing function of u, and the total medical cost for patient i is Xi (Ti ). The parameter of interest is given by β = E{X(T )}. If we make no assumptions regarding the joint distribu¯ )}, we showed in Theorem 4.4 that the tangent space for this tion of {T, X(T nonparametric model is the entire Hilbert space. Consequently, using arguments in Section 5.3, where we derived the inﬂuence function for the mean of a random variable under a nonparametric model, there can be at most one inﬂuence function of an RAL estimator for β. With a sample of iid data ¯ i (Ti )}, i = 1, . . . , n, the obvious estimator for the mean medical costs {Ti , X is n −1 ˆ βn = n Xi (Ti ), i=1

which has inﬂuence function X(T ) − E{X(T )}. If, however, the duration of illness is right censored for some individuals, then this problem becomes more

214

9 Augmented Inverse Probability Weighted Complete-Case Estimators

diﬃcult. We will use this example for illustration as we develop censored data estimators. In actuality, we often don’t observe the full data because of censoring, possibly because of incomplete follow-up of the patients due to staggered entry and ﬁnite follow-up, or because the patients drop out of the study prematurely. To accommodate censoring, we introduce a censoring variable C˜ that corresponds to the time at that an individual would be censored from the study. Remark 3. Notational convention Since we have been using C (scripted C) to denote the diﬀerent levels of coarsened data and because censoring is typically denoted by the variable C (unscripted), the diﬀerence may be diﬃcult to discern. Therefore, we will use C˜ (i.e., C with a tilde over it) to denote the censoring variable from here on. We assume that underlying any problem in survival analysis there are unobservable latent random variables ¯ i (Ti )}, i = 1, . . . , n. {C˜i , Ti , X ¯(t)} can be written as The joint distribution of pC,T, ˜ ¯ ) {c, t, x X(T pC|T, ¯(t)}pT,X(T ¯(t)}, ¯ ) {t, x ˜ ¯ ) {c|t, x X(T where pT,X(T ¯(t)} denotes the density of the full data had we been able ¯ ) {t, x to observe them. The observed (coarsened) data for this problem are denoted as ¯ )}, {U, ∆, X(U ˜ (observed time on study), ∆ = I(T ≤ C) ˜ (censoring where U = min(T, C) ¯ ) is the history of the time-dependent covariates while indicator), and X(U on study. Coarsening of the data in this case is related to the fact that we don’t observe the full data because of censoring. We will show that censoring can be mapped to a form of monotone coarsening. The coarsening variable C, which we took previously to be discrete, is now a continuous variable because of continuous-time censoring. A complete case, C = ∞, corresponds to ∆ = 1 ˜ or, equivalently, to (T ≤ C). To make the connection between censoring and the coarsening notation used previously, we deﬁne (C = r) to be (C˜ = r, T > r) and, when C = ¯ )} = X{min(r, ¯ r, we observe Gr {T, X(T T )}, T I(T ≤ r) for r < ∞ and ¯ )} = {T, X(T ¯ )}. With this notation G∞ {T, X(T ¯ )} = G∞ {T, X(T ¯ )} = {T, X(T ¯ )} whenever r ≥ T. Gr {T, X(T

9.3 Censoring and Its Relationship to Monotone Coarsening

215

Nonetheless, this still satisﬁes all the assumptions of monotone coarsening. Therefore, the observed data can be expressed as ¯ )}, [C, GC {T, X(T where ¯ )}] = {C˜ = r, T > r, X(r)} ¯ [C = r, Gr {T, X(T and ¯ )}] = {T ≤ C, ˜ T, X(T ¯ )}. [C = ∞, G∞ {T, X(T With monotone coarsening, we argued that it is more convenient to work with hazard functions in describing the coarsening probabilities. With a slight abuse of notation, the coarsening hazard function is given as ¯ )} = P {C = r|C ≥ r, T, X(T ¯ )}, λr {T, X(T

r < ∞.

(9.27)

Remark 4. Because the censoring variable C˜ is a continuous random variable, so is the corresponding coarsening variable C. Therefore, the hazard function given by (9.27), which strictly speaking is used for discrete coarsening, has to be deﬁned in terms of a continuous-time hazard function; namely, ¯ )} = lim h−1 P {r ≤ C =< r + h|C ≥ r, T, X(T ¯ )}, λr {T, X(T h→0

r < ∞.

However, unless we need further clariﬁcation, it will be convenient to continue with this abuse of notation. The event C ≥ r, which includes C = ∞, is equal to ˜ ∪ (T < C). ˜ (C ≥ r) = (C˜ ≥ r, T > C)

(9.28)

Therefore, ¯ )} = P [C˜ = r, T ≥ r|{(C˜ ≥ r, T > C) ˜ ∪ (T < C)}, ˜ T, X(T ¯ )]. λr {T, X(T (9.29) If T < r, then (9.29) must equal zero, whereas if T ≥ r, then {(C˜ ≥ r, T > ˜ ∪ (T < C)} ˜ ∩ (T ≥ r) = (C˜ ≥ r). Consequently, C) ¯ )} = P {C˜ = r|C˜ ≥ r, T, X(T ¯ )} I(T ≥ r). λr {T, X(T * +, || this is the hazard function of ¯ ). censoring at time r given T, X(T If, in addition, we make the coarsening at random (CAR) assumption, ¯ )} = λr [Gr {T, X(T ¯ )}], λr {T, X(T then ¯ )}] = P {C˜ = r|C˜ ≥ r, T ≥ r, X(r)}I(T ¯ ≥ r). λr [Gr {T, X(T

216

9 Augmented Inverse Probability Weighted Complete-Case Estimators

Let us denote the hazard function for censoring by ¯ ¯ = P {C˜ = r|C˜ ≥ r, T ≥ r, X(r)}. λC˜ {r, X(r)} Then ¯ )}] = λ ˜ {r, X(r)}I(T ¯ ≥ r). λr [Gr {T, X(T C

(9.30)

In order to construct estimators for a full-data parameter using coarsened data, such as those given by (9.4), we need to compute the probability ¯ )}] = P {C = ∞|T, X(T ¯ )} = P {∆ = of a complete case [∞, G∞ {T, X(T ¯ 1|T, X(T )} and a typical element of the augmentation space, Λ2 . We now show how these are computed with censored data using the hazard function for the censoring time and counting process notation. Probability of a Complete Case with Censored Data For discrete monotone coarsening, we showed in (8.6) how the probability of a complete case C = ∞ can be written in terms of the discrete hazards. For a continuous-time hazard function, the analogous relationship is given by ! ¯ )}] = P {∆ = 1|T, X(T ¯ )} = ¯ )}]dr [∞, G∞ {T, X(T 1 − λr [Gr {T, X(T

−

= exp

−

= exp = exp

0

∞

0

−

r<∞ ∞

0

T

¯ )}]dr λr [Gr {T, X(T ¯ λC˜ {r, X(r)}I(T ≥ r)dr

¯ λC˜ {r, X(r)}dr .

(9.31)

The Augmentation Space, Λ2 , with Censored Data In equation (9.20) of Theorem 9.2, we showed that an arbitrary element of the augmentation space, Λ2 , with monotone coarsening can be written as I(C = r) − λr [Gr {T, X(T ¯ )}]I(C ≥ r) ¯ )}]. Lr [Gr {T, X(T ¯ K [G {T, X(T )}] r r r=∞

(9.32)

Using counting process notation, we denote the counting process corresponding to the number of observed censored observations up to and including time ˜ Consequently, r by NC˜ (r) = I(U ≤ r, ∆ = 0) = I(C˜ ≤ r, T > C). I(C = r) = I(C˜ = r, T > r) = dNC˜ (r), where dNC˜ (r) denotes the increment of the counting process. Using (9.30), we obtain

9.3 Censoring and Its Relationship to Monotone Coarsening

217

¯ )}]I(C ≥ r) = λ ˜ {r, X(r)}I(T ¯ λr [Gr {T, X(T ≥ r)I(C˜ ≥ r). C Letting Y (r) denote the at-risk indicator, Y (r) = I(U ≥ r) = I(T ≥ r, C˜ ≥ r), we obtain ¯ )}]I(C ≥ r) = λ ˜ {r, X(r)}Y ¯ λr [Gr {T, X(T (r). C Because the elements in the sum of (9.32) are nonzero only if T ≥ r and C˜ ≥ r, ¯ )}] and Kr [Gr {T, X(T ¯ )}] as Lr {X(r)} ¯ it suﬃces to deﬁne Lr [Gr {T, X(T and ¯ Kr {X(r)}, respectively. Moreover, ¯ Kr {X(r)} =

!

¯ 1 − λC˜ {u, X(u)}du

= exp −

u≤r

0

r

¯ λC˜ {u, X(u)}du .

(9.33) Consequently, a typical element of Λ2 , given by (9.32), can be written using stochastic integrals of counting process martingales, namely ∞ 0

¯ dMC˜ {r, X(r)} ¯ Lr {X(r)}, ¯ Kr {X(r)}

where ¯ ¯ dMC˜ {r, X(r)} = dNC˜ (r) − λC˜ {r, X(r)}Y (r)dr ¯ is the usual counting process martingale increment and Kr {X(r)} is given by (9.33). Deriving Estimators with Censored Data Suppose we want to estimate the parameter β in a survival problem with ¯ i (Ui )}, i = 1, . . . , n. Let us assume that we know censored data {Ui , ∆i , X ¯ i (Ti )}, i = 1, . . . , n; say, how to estimate β if we had full data Zi = {Ti , X for example, we had an unbiased estimating function m(Zi , β) that we could use as a basis for deriving an m-estimator. In addition, let us assume that ¯ we knew the hazard function for censoring, λC˜ {r, X(r)}. Since censoring is a special case of coarsened data, we could use (9.4) to obtain estimators for β with censored data. Speciﬁcally, using the notation developed above, an AIPWCC estimator for β can be derived by solving the estimating equation n i=1

∆i ¯ i (Ui )} m(Zi , β) + KUi {X

∞ 0

¯ i (r)} dMC˜i {r, X ¯ { X (r)} = 0, L r i ¯ i (r)} Kr {X

(9.34)

¯ where Kr {X(r)} is given by (9.33). We now return to Example 2, where the goal was to estimate the mean medical costs of patients with some illness during the course of their illness. We deﬁned the parameter of interest as β = E{X(T )}, where X(T ) was the

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9 Augmented Inverse Probability Weighted Complete-Case Estimators

accumulated medical costs of a patient during the time T of his or her illness. As noted previously, with full data there is only one inﬂuence function of RAL estimators for β, namely X(T ) − β. Consequently, the obvious full-data estimating function for this problem is m(Zi , β) = Xi (Ti ) − β. With censored data, we use (9.34) to derive an arbitrary estimator for β as the solution to n i=1

∆i ¯ i (Ui )} {Xi (Ui ) − β} + KUi {X

This leads to the estimator n ∆i i=1

βˆn =

¯ i (Ui )} Xi (Ui ) KUi {X

n

+

∞ 0

¯ i (r)} dMC˜i {r, X ¯ { X (r)} = 0. L r i ¯ i (r)} Kr {X

∞ dMC˜i {r,X¯ i (r)} 0

¯ i (r)} Kr {X

∆i ¯ i (Ui )} i=1 KU {X i

¯ i (r)} Lr {X .

(9.35)

Notice that, unlike the case where there was only one inﬂuence function with the full data for this problem, there are many inﬂuence functions with censored data. The observed (censored) data inﬂuence functions depend on the ¯ choice of Lr {X(r)} for r ≥ 0, which, in turn, aﬀects the asymptotic variance of the corresponding estimator in (9.35). Clearly, we want to choose such functions in order to minimize the asymptotic variance of the corresponding estimator. This issue will be studied carefully in the next chapter. We also note that the estimator given above assumes that we know the ¯ hazard function for censoring, λC˜ {r, X(r)}. In practice, this will be unknown to us and must be estimated from the observed data. If the censoring time ¯ )}, then one can estimate Kr using C˜ is assumed independent of {T, X(T ˜ see Kaplan and Meier the Kaplan-Meier estimator for the censoring time C; (1958). If the censoring time is related to the time-dependent covariates, then a model has to be developed. A popular model for this purpose is Cox’s proportional hazards model (Cox, 1972, 1975). If Lr (·) is taken to be identically equal to zero, then we obtain the IPWCC estimator. This estimator, referred to as the simple weighted estimator for estimating the mean medical cost with censored cost data, was studied by Bang and Tsiatis (2000), who also derived the large-sample properties. More eﬃcient estimators with a judicious choice of Lr (·) were also proposed in that paper.

9.4 Recap and Review of Notation Constructing AIPWCC estimators •

Let m(Z, β) be a full-data estimating function, chosen so that m(Z, β0 ) ∈ ΛF ⊥ .

9.4 Recap and Review of Notation

•

219

If coarsening probabilities are known by design (i.e., ψ0 known), then an observed-data RAL AIPWCC estimator for β is obtained as the solution to the estimating equation n I(Ci = ∞)m(Zi , β) + L2 {Ci , GCi (Zi ), ψ0 } = 0, (∞, Zi , ψ0 ) i=1 and the i-th inﬂuence function of the resulting estimator βˆn is ϕ{C ˜ i , GCi (Zi ), ψ0 } = −1 I(Ci = ∞)m(Zi , β0 ) ∂m(Zi , β0 ) − E {C , G (Z ), ψ } , + L 2 i C i 0 i ∂β T (∞, Zi , ψ0 ) where L2 (·) is an element of the augmentation space given by ⎡ ⎤ I(C = ∞) ⎣ {r, Gr (Z), ψ0 }L2r {Gr (Z)}⎦ (∞, Z, ψ0 ) r=∞ − I(C = r)L2r {Gr (Z)}, r=∞

for arbitrarily chosen functions L2r {Gr (Z)}, r = ∞. • If the coarsening probabilities need to be modeled (i.e., ψ is unknown), then an observed-data RAL AIPWCC estimator for β is obtained as the solution to the estimating equation # $ n I(Ci = ∞)m(Zi , β) + L2 {Ci , GCi (Zi ), ψˆn } = 0, (∞, Zi , ψˆn ) i=1

where ψˆn denotes the MLE for ψ. The i-th inﬂuence function of the resulting estimator is ϕ{C ˜ i , GCi (Zi ), ψ0 } − E(ϕS ˜ ψT ){E(Sψ SψT )}−1 Sψ {Ci , GCi (Zi ), ψ0 } = ϕ{C ˜ i , GCi (Zi ), ψ0 } − Π[ϕ{C ˜ i , GCi (Zi ), ψ0 }|Λψ ]. The augmentation space with monotone coarsening •

When the observed data are monotonically coarsened, then it will prove to be convenient to express the elements of the augmentation space using discrete hazard functions, speciﬁcally an element L2 {C, GC (Z), ψ} ∈ Λ2 , I(C = r) − λr {Gr (Z), ψ}I(C ≥ r) Lr {Gr (Z)}, Kr {Gr (Z), ψ} r=∞

for arbitrarily chosen functions Lr {Gr (Z)} r = ∞.

220

9 Augmented Inverse Probability Weighted Complete-Case Estimators

9.5 Exercises for Chapter 9 1. In equation (9.4), we proposed an m-estimator (AIPWCC) for β when the coarsening probabilities are known by design. a) Derive the inﬂuence function for this estimator and demonstrate that it equals (9.5). b) Derive a consistent estimator for the asymptotic variance of this estimator.

10 Improving Eﬃciency and Double Robustness with Coarsened Data

Thus far, we have described the class of observed-data inﬂuence functions when data are coarsened at random (CAR) by taking advantage of results obtained for a full-data semiparametric model. We also illustrated how these results can be used to derive estimators using augmented inverse probability weighted complete-case estimating equations. The results were geometric, relying on our ability to deﬁne the spaces ΛF ⊥ ⊂ HF and Λ2 ⊂ H. Ultimately, the goal is to derive as eﬃcient an estimator for β as is possible using coarsened data. As we will see, this exercise will be primarily theoretical, as it will most often be the case that we cannot feasibly construct the most eﬃcient estimator. Nonetheless, the study of eﬃciency with coarsened data will aid us in constructing more eﬃcient estimators even if we are not able to derive the most eﬃcient one.

10.1 Optimal Observed-Data Inﬂuence Function Associated with Full-Data Inﬂuence Function We have already shown in (8.19) that all observed-data inﬂuence functions of RAL estimators for β can be written as I(C = ∞)ϕF (Z) + L2 {C, GC (Z)} − Π{[ · ]|Λψ }, (10.1) ϕ{C, GC (Z)} = (∞, Z, ψ0 ) where ϕF (Z) is a full-data inﬂuence function and L2 {C, GC (Z)} ∈ Λ2 . We know that the asymptotic variance of an RAL estimator for β is the variance of its inﬂuence function. Therefore, we now consider how to derive the optimal observed-data inﬂuence function within the class of inﬂuence functions (10.1) for a ﬁxed full-data inﬂuence function ϕF (Z) ∈ (IF )F , where optimal refers to the element with the smallest variance matrix.

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Theorem 10.1. The optimal observed-data inﬂuence function among the class of observed-data inﬂuence functions given by (10.1) for a ﬁxed ϕF (Z) ∈ F (Z) (IF )F is obtained by choosing L2 {C, GC (Z)} = −Π[ I(C=∞)ϕ (∞,Z,ψ0 ) |Λ2 ], in which case the optimal inﬂuence function is given by I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) Λ2 . (10.2) −Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) Proof. We begin by noting that the space of elements in (10.1), for a ﬁxed ϕF (Z), is a linear variety as deﬁned by Deﬁnition 7 of Chapter 3 (i.e., a translation of a linear space away from the origin). Speciﬁcally, this space is given as V = x0 + M , where the element I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) Λψ x0 = −Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) and the linear subspace

M = Π[Λ2 |Λ⊥ ψ ].

/ M. To prove that this space is a linear variety, we must show that x0 ∈ By Theorem 8.1, we know that Λψ ⊂ Λ2 . Therefore, it suﬃces to show that x0 ∈ / Λ2 . This follows because E(x0 |Z) = ϕF (Z) = 0. It is also straightforward to show that the linear space M is a q-replicating linear space as deﬁned by Deﬁnition 6 of Chapter 3. (We leave this as an exercise for the reader.) Consequently, as a result of Theorem 3.3, the element in this linear variety with the smallest variance matrix is given as x0 − Π[x0 |M ]. The theorem will follow if we can prove that I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) Λ2 − Π Λψ . Π[x0 |M ] = Π (∞, Z, ψ0 ) (∞, Z, ψ0 )

(10.3)

In order to prove that (10.3) is a projection, we must show that (a) (10.3) is an element of M and (b) x0 − Π[x0 |M ] is orthogonal to M . F (Z) (a) follows because Π I(C=∞)ϕ Λ 2 ∈ Λ2 and (∞,Z,ψ0 ) I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) Π Λψ = Π Π Λ2 Λψ , (∞, Z, ψ0 ) (∞, Z, ψ0 ) where (10.4) follows because Λψ ⊂ Λ2 . (b) follows because

(10.4)

10.1 Observed-Data Inﬂuence Function with a Full-Data Inﬂuence Function

x0 − Π[x0 |M ] =

223

I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) −Π Λ2 , (∞, Z, ψ0 ) (∞, Z, ψ0 )

which is an element orthogonal to Λ2 and hence orthogonal to M because Λψ ⊂ Λ2 . Remark 1. At the end of Section 9.1, we made the observation that the asymptotic variance of an observed-data estimator for β would be more eﬃcient if the parameter ψ in a model for the coarsening probabilities were estimated even if in fact this parameter was known by design. This stemmed from the fact that estimating the parameter ψ resulted in an inﬂuence function that subtracted oﬀ the projection of the term in the square brackets of (10.1) onto Λψ . However, we now realize that if we constructed the estimator for β as eﬃciently as possible when ψ is known (that is, if we chose F (Z) L2 {C, GC (Z)} = −Π[ I(C=∞)ϕ (∞,Z,ψ0 ) |Λ2 ]), then the term in the square brackets of (10.1) would equal I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) −Π Λ2 , (∞, Z, ψ0 ) (∞, Z, ψ0 ) which is orthogonal to Λ2 and hence orthogonal to Λψ , in which case the additional projection onto Λψ that comes from estimating ψ would equal zero and therefore would not result in any gain in eﬃciency. A linear operator was deﬁned in Chapter 7 (see Deﬁnition 1). It will be convenient to deﬁne the mapping from a full-data inﬂuence function to the corresponding optimal observed-data inﬂuence function given by Theorem 10.1 using a linear operator. Deﬁnition 1. The linear operator J : HF → H is deﬁned so that for any element hF (Z) ∈ HF , I(C = ∞)hF (Z) I(C = ∞)hF (Z) −Π Λ2 . (10.5) J (hF ) = (∞, Z, ψ0 ) (∞, Z, ψ0 ) Using this deﬁnition, we note that the optimal observed-data inﬂuence function within the class (10.1), for a ﬁxed ϕF (Z) ∈ (IF )F , is given by J (ϕF ). Since any observed-data inﬂuence function of an RAL estimator for β must be an element within the class (10.1) for some ϕF (Z) ∈ (IF )F , if we want to ﬁnd the eﬃcient inﬂuence function, it suﬃces to restrict attention to the class of inﬂuence functions J (ϕF ) for ϕF (Z) ∈ (IF )F . We deﬁne the space of such inﬂuence functions as follows. Deﬁnition 2. We denote the space J {(IF )F }, the space whose elements are F F F J (ϕ ) for all ϕ (Z) ∈ (IF ) , by (IF )DR .

224

10 Improving Eﬃciency and Double Robustness with Coarsened Data

The subscript “DR” is used to denote double robustness. Therefore, the space (IF )DR ⊂ (IF ) is deﬁned as the set of double-robust observed-data inﬂuence functions. The term double robust was ﬁrst introduced in Section 6.5. Why we refer to these as double-robust inﬂuence functions will become clear later in the chapter. Since the space of full-data inﬂuence functions is a linear variety in HF (see Theorem 4.3) (IF )F = ϕF (Z)+T F ⊥ , where ϕF (Z) is an arbitrary full-data inﬂuence function and T F is the full-data tangent space, it is clear that (IF )DR = J {(IF )F } = J (ϕF ) + J (T F ⊥ ) is a linear variety in H. Remark 2. As we have argued repeatedly, because an inﬂuence function of an observed-data RAL estimator for β can be obtained, up to a proportionality constant, from an element orthogonal to the observed-data nuisance tangent space, this has motivated us to deﬁne estimating functions m{C, GC (Z), β}, where m{C, GC (Z), β0 } ∈ Λ⊥ . If, however, we are interested in deriving observed-data RAL estimators for β whose inﬂuence function belongs to (IF )DR , then we should choose an estimating function m{C, GC (Z), β} so that m{C, GC (Z), β0 } is an element of the linear space J (ΛF ⊥ ). This follows because it suﬃces to deﬁne estimating functions that at the truth are proportional to inﬂuence functions. Since any element ϕ∗F (Z) ∈ ΛF ⊥ properly normalized will lead to a full-data inﬂuence function ϕF (Z) = Aq×q ϕ∗F (Z), where A = {E(ϕ∗F SβT )}−1 , and because J (·) is a linear operator, this implies that a typical element J (ϕF ) of (IF )DR is equal to J (Aϕ∗F ) = AJ (ϕ∗F ); i.e., it is proportional to an element J (ϕ∗F ) ∈ J (ΛF ⊥ ). We deﬁne the space J (ΛF ⊥ ) to be the DR linear space. Deﬁnition 3. The linear subspace J (ΛF ⊥ ) ⊂ Λ⊥ ⊂ H, the space that consists of elements J (ϕ∗F ) : ϕ∗F (Z) ∈ ΛF ⊥ , will be referred to as the DR linear space.

This now gives us a prescription for how to ﬁnd observed-data RAL estimators for β whose inﬂuence function belongs to (IF )DR . We start by choosing a full-data estimating function m(Z, β) so that m(Z, β0 ) = ϕ∗F (Z) ∈ ΛF ⊥ . We then construct the observed-data estimating function m{C, GC (Z), β} = J {m(Z, β)}, where I(C = ∞)m(Z, β) I(C = ∞)m(Z, β) Λ2 . J {m(Z, β)} = −Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) If the observed data were coarsened by design (i.e., ψ0 known), then we would derive an estimator for β by solving the estimating equation n I(Ci = ∞)m(Zi , β) + L2 {Ci , GCi (Zi ), β, ψ0 } = 0, (∞, Zi , ψ0 ) i=1

(10.6)

10.2 Improving Eﬃciency with Two Levels of Missingness

where

225

I(C = ∞)m(Z, β) Λ2 . L2 {C, GC (Z), β, ψ} = −Π (∞, Z, ψ)

If the coarsening probabilities were not known and had to be modeled using the unknown parameter ψ, then we would derive an estimator for β by solving the estimating equation n I(Ci = ∞)m(Zi , β) (10.7) + L2 {Ci , GCi (Zi ), β, ψˆn } = 0, (∞, Zi , ψˆn ) i=1 where ψˆn is the MLE for the parameter ψ and L2 (·) is deﬁned as above. Finding projections onto the augmentation space Λ2 is not necessarily easy. Later we will discuss a general procedure for ﬁnding such projections that involves an iterative process. However, in the case when there are two levels of coarsening or when the coarsening is monotone, a closed-form solution for the projection onto Λ2 exists. We will study these two scenarios more carefully. We start by illustrating how to ﬁnd improved estimators using these results when there are two levels of missingness.

10.2 Improving Eﬃciency with Two Levels of Missingness Let the data for a single observation Z be partitioned as (Z1T , Z2T )T , where we always observe Z1 but Z2 may be missing for some individuals. For this problem, it is convenient to use R to denote the complete-case indicator; that is, if R = 1, we observe Z = (Z1T , Z2T )T , whereas if R = 0 we only observe Z1 . The observed data are given by Oi = (Ri , Z1i , Ri Z2i ), i = 1, . . . , n. The assumption of missing at random implies that P (R = 0|Z1 , Z2 ) = P (R = 0|Z1 ), which, in turn, implies that P (R = 1|Z1 , Z2 ) = P (R = 1|Z1 ), which we denote by π(Z1 ). Such complete-case probabilities may be known to us by design or may have to be estimated using a model for the missingness probabilities that includes the additional parameter ψ. In the latter case, since R is binary, a logistic regression model is often used; for example, π(Z1 , ψ) =

exp(ψ0 + ψ1T Z1 ) . 1 + exp(ψ0 + ψ1T Z1 )

(10.8)

The parameter ψ = (ψ0 , ψ1T )T can be estimated using a maximum likelihood estimator; that is, by maximizing n ! exp{(ψ0 + ψ T Z1i )Ri } 1

i=1

1 + exp(ψ0 + ψ1T Z1i )

(10.9)

using the data (Ri , Z1i ), i = 1, . . . , n, which are available on everyone. In equation (10.8), we used a logistic regression model that was linear in Z1 ;

226

10 Improving Eﬃciency and Double Robustness with Coarsened Data

however, we can make the model as ﬂexible as necessary to ﬁt the data. For example, we can include higher-order polynomial terms, interaction terms, splines, etc. As always, there is an underlying full-data model Z ∼ p(z, β, η) ∈ P, where β is the q-dimensional parameter of interest and η is the nuisance parameter (possibly inﬁnite-dimensional), and our goal is to estimate β using the observed data Oi = (Ri , Z1i , Ri Z2i ), i = 1, . . . , n. In order to use either estimating equation (10.6) or (10.7), when ψ is known or unknown, respectively, to obtain an observed-data RAL estimator for β whose inﬂuence function is an element of (IF )DR , we must ﬁnd the projection ∗F (Z) onto the augmentation space Λ2 , where ϕ∗F (Z) = m(Z, β0 ) ∈ of I(C=∞)ϕ (∞,Z) F⊥ Λ . Using the notation for two levels of missingness, we now consider how ∗F (Z) onto the augmentation space. According to derive the projection of Rϕ π(Z1 ) to (7.40) of Chapter 7, we showed that, with two levels of missingness, Λ2 consists of the set of elements R − π(Z1 ) h2 (Z1 ), L2 (O) = π(Z1 ) (Z1 ) is an arbitrary q-dimensional function of Z1 . where hq×1 2 Finding the Projection onto the Augmentation Space ∗F

(Z) Theorem 10.2. The projection of Rϕ onto the augmentation space Λ2 π(Z1 ) R−π(Z1 ) h02 (Z1 ) ∈ Λ2 , where is the unique element π(Z1 )

h02 (Z1 ) = E{ϕ∗F (Z)|Z1 }.

(10.10)

∗F

(Z) Proof. The projection of Rϕ onto the space Λ2 is the unique element π(Z1 ) R−π(Z1 ) h02 (Z1 ) ∈ Λ2 such that the residual π(Z1 )

Rϕ∗F (Z) − π(Z1 )

R − π(Z1 ) π(Z1 )

h02 (Z1 )

is orthogonal to every element in Λ2 ; that is, T R − π(Z1 ) h02 (Z1 ) h2 (Z1 ) = 0 π(Z1 ) (10.11) for all functions h2 (Z1 ). We derive the expectation in (10.11) by using the law of iterated conditional expectations, where we ﬁrst condition on Z = (Z1T , Z2T )T to obtain

E

Rϕ∗F (Z) − π(Z1 )

R − π(Z1 ) π(Z1 )

10.2 Improving Eﬃciency with Two Levels of Missingness

R − π(Z1 ) R Z ϕ∗F (Z) E π(Z1 ) π(Z1 ) 2 T R − π(Z1 ) 0 −E Z h2 (Z1 ) h2 (Z1 ) . π(Z1 )

227

E

(10.12)

Because E

2 R{R − π(Z1 )} R − π(Z1 ) 1 − π(Z1 ) Z = E , Z = π 2 (Z1 ) π(Z1 ) π(Z1 )

we write (10.12) as E

1 − π(Z1 ) π(Z1 )

ϕ

∗F

T

(Z) −

h02 (Z1 )

h2 (Z1 ) .

(10.13)

Therefore, we must ﬁnd the function h02 (Z1 ) such that (10.13) is equal to zero for all h2 (Z1 ). We derive (10.13) by again using the law of iterated conditional expectations, where we ﬁrst condition on Z1 to obtain E

1 − π(Z1 ) π(Z1 )

T E{ϕ∗F (Z)|Z1 } − h02 (Z1 ) h2 (Z1 ) .

(10.14)

1) By assumption, π(Z1 ) > for all Z1 , which implies that 1−π(Z π(Z1 ) is bounded away from zero and ∞ for all Z1 . Therefore, equation (10.14) will be equal to zero for all h2 (Z1 ) if and only if

h02 (Z1 ) = E{ϕ∗F (Z)|Z1 }. Thus, among estimators whose estimating functions are based on elements of Λ⊥ , R − π(Z1 ) Rϕ∗F (Z) − h2 (Z1 ) − Π{[·]|Λψ }, π(Z1 ) π(Z1 ) for a ﬁxed ϕ∗F (Z) ∈ ΛF ⊥ , the one that gives the optimal answer (i.e., the estimator with the smallest asymptotic variance) is obtained by choosing h2 (Z1 ) = E{ϕ∗F (Z)|Z1 }. This result, although interesting, is not of practical use since we don’t know the conditional expectation E{ϕ∗F (Z)|Z1 }. The result does, however, suggest that we consider an adaptive approach where we use the data to estimate this conditional expectation, as we now illustrate. Adaptive Estimation Since Z = (Z1T , Z2T )T , in order to compute the conditional expectation of E{ϕ∗F (Z)|Z1 }, we need to know the conditional density of Z2 given Z1 . Of

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

course, this conditional distribution depends on the unknown full-data parameters β and η. As we have argued, estimating β and η using likelihood methods for semiparametric models (i.e., when the parameter η is inﬁnitedimensional) may be diﬃcult, if not impossible, using coarsened data. This is the reason we introduced AIPWCC estimators in the ﬁrst place. Another approach, which we now advocate, is to be adaptive. That is, we posit a working model where the density of Z is assumed to be within the model p∗Z1 ,Z2 (z1 , z2 , ξ) ∈ Pξ ⊂ P, where the parameter ξ is ﬁnite-dimensional. The conditional density of Z2 given Z1 as a function of the parameter ξ would be p∗Z2 |Z1 (z2 |z1 , ξ) =

p∗Z1 ,Z2 (z1 , z2 , ξ) . p∗Z1 ,Z2 (z1 , u, ξ)dνZ2 (u)

For such a model, the parameter ξ can be estimated by maximizing the observed-data likelihood (7.10), which, with two levels of missingness, can be written as 1−Ri n ! p∗Z1 ,Z2 (Z1i , u, ξ)dνZ2 (u) p∗Z1 ,Z2 (Z1i , Z2i , ξ)Ri . (10.15) i=1

Since we only need the conditional distribution of Z2 given Z1 to derive the desired projection, an even simpler approach would be to posit a parametric model for the conditional density of Z2 given Z1 in terms of a parametric model with a ﬁnite number of parameters ξ. Let us denote such a posited model by p∗Z2 |Z1 (z2 |z1 , ξ). Because of the missing at random (MAR) assumption, R is conditionally independent of Z2 given Z1 . This is denoted as R ⊥ ⊥ Z2 |Z1 . Consequently, pZ2 |Z1 ,R (z2 |z1 , r) = pZ2 |Z1 ,R=1 (z2 |z1 , r = 1) = pZ2 |Z1 (z2 |z1 ). Therefore, it suﬃces to consider only the complete cases {i : Ri = 1} because the conditional distribution of Z2 given Z1 among the complete cases is the same as the conditional distribution of Z2 given Z1 in the population. A natural estimator for ξ would be to maximize the conditional likelihood of Z2 given Z1 in ξ among the complete cases. Namely, we would estimate ξ by maximizing ! p∗Z2 |Z1 (z2i |z1i , ξ). (10.16) i:Ri =1

The resulting estimator is denoted by ξˆn∗ . Remark 3. Whether we posit a simpler model for the density of Z = (Z1T , Z2T )T or the conditional density of Z2 given Z1 , we must keep in mind that this is a posited model and that the true conditional density p0Z2 |Z1 (z2 |z1 ) may not be contained in this model. Moreover, if we develop a model directly for the conditional density of Z2 given Z1 , then we must be careful that such a model is consistent with the underlying semiparametric model.

10.2 Improving Eﬃciency with Two Levels of Missingness

229

Nonetheless, with the adaptive approach, we proceed as if the posited model were correct and estimate ξ using the observed data. Under suitable regularity conditions, this estimator will converge in probability to some conP stant ξ ∗ ; i.e., ξˆn∗ → ξ ∗ and n1/2 (ξˆn∗ − ξ ∗ ) will be bounded in probability. In general, the posited model will not contain the truth, in which case p∗Z2 |Z1 (z2 |z1 , ξ ∗ ) = p0Z2 |Z1 (z2 |z1 ). If, however, our posited model did contain the truth, then we denote this by taking ξ ∗ to equal ξ0 , where p∗Z2 |Z1 (z2 |z1 , ξ0 ) = p0Z2 |Z1 (z2 |z1 ). With such a posited model for the conditional density of Z2 given Z1 and an estimator ξˆn∗ , we are able to estimate h02 (Z1 ) = E{ϕ∗F (Z)|Z1 } by using h∗2 (Z1 , ξˆn∗ ) = ϕ∗F (Z1 , u)p∗Z2 |Z1 (u|Z1 , ξˆn∗ )dνZ2 (u). Again, keep in mind that h∗2 (Z1 , ξˆn∗ ) → h∗2 (Z1 , ξ ∗ ) =

ϕ∗F (Z1 , u)p∗Z2 |Z1 (u|Z1 , ξ ∗ )dνZ2 (u),

where h∗2 (Z1 , ξ ∗ ) is a function of Z1 but not necessarily that h02 (Z1 ) = h∗2 (Z1 , ξ ∗ ) unless the posited model for the conditional density of Z2 given Z1 was correct. With this as background, we now give a step-by-step algorithm on how to derive an improved estimator. In so doing, we consider the scenario where the parameter ψ in our missingness model is unknown and must be estimated. Algorithm for Finding Improved Estimators with Two Levels of Missingness 1. We ﬁrst consider how the parameter β would be estimated if there were no missing data (i.e., the full-data problem). That is, we choose an estimating function, say m(Z, β), where m(Z, β0 ) = ϕ∗F (Z) ∈ ΛF ⊥ . We might consider using an estimating function that leads to an eﬃcient or locally eﬃcient full-data estimator for β. However, we must keep in mind that the eﬃcient full-data inﬂuence function may not be the one that leads to an eﬃcient observed-data inﬂuence function. A detailed discussion of this issue will be deferred until the next chapter. 2. We posit a model for the complete-case (missingness) probabilities in terms of the parameter ψ, say P (R = 1|Z) = π(Z1 , ψ), and using the data (Ri , Z1i ), i = 1, . . . , n, which are available on the entire sample, we derive the maximum likelihood estimator ψˆn for ψ. For example, we might use the

230

10 Improving Eﬃciency and Double Robustness with Coarsened Data

logistic regression model in (10.8), in which case the estimator is obtained by maximizing (10.9). In general, however, we would maximize n !

{π(Z1i , ψ)}Ri {1 − π(Z1i , ψ)}1−Ri .

i=1

We denote the MLE by ψˆn . 3. We posit a model for either the distribution of Z = (Z1T , Z2T )T or the conditional distribution of Z2 given Z1 in terms of the parameter ξ. Either way, this results in a model for p∗Z2 |Z1 (z2 |z1 , ξ) in terms of the parameter ξ. Using the observed data, we derive the MLE ξˆn∗ for ξ by maximizing either (10.15) or (10.16). 4. The estimator for β is obtained by solving the estimating equation " n Ri m(Zi , β) Ri − π(Z1i , ψˆn ) (10.17) − h∗2 (Z1i , β, ξˆn∗ ) = 0, π(Z1i , ψˆn ) π(Z1i , ψˆn ) i=1 where h∗2 (Z1i , β, ξ) = E m(Zi , β)|Z1i , ξ = m(Z1i , u, β)p∗Z2 |Z1 (u|Z1i , ξ)dνZ2 (u). Remarks Regarding Adaptive Estimators The semiparametric theory that we have developed for coarsened data implicitly assumes that the model for the coarsening probabilities is correctly speciﬁed. With two levels of missingness, this means that P (R = 1|Z1 ) = π0 (Z1 ) is contained within the model π(Z1 , ψ), in which case, under suitable regularity conditions, π(Z1 , ψˆn ) → π0 (Z1 ), where ψˆn denotes the MLE for ψ. The fact that we used the augmented term " Ri − π(Z1i , ψˆn ) (10.18) − h∗2 (Z1i , β, ξˆn∗ ) π(Z1i , ψˆn ) in equation (10.17) was an attempt to gain eﬃciency from the data that are incomplete (i.e., {i : Ri = 0}). To get the greatest gain in eﬃciency, the augmented term must equal " Ri − π(Z1i , ψˆn ) − h02 (Z1i , β), π(Z1i , ψˆn ) where

10.2 Improving Eﬃciency with Two Levels of Missingness

h02 (Z1i , β) = E0 {m(Zi , β)|Z1i },

231

(10.19)

where the conditional expectation on the right-hand side of (10.19) is with respect to the true conditional density of Z2 given Z1 . Because we are using a posited model, the function h∗2 (Z1i , β, ξˆn∗ ) will converge to h∗2 (Z1i , β, ξ ∗ ), which is not equal to the desired h02 (Z1i , β) unless the posited model was correct. Nonetheless, h∗2 (Z1i , β, ξ ∗ ) is a function of Z1i , in which case Ri − π(Z1i , ψ0 ) h∗2 (Z1i , β0 , ξ ∗ ) ∈ Λ2 . − π(Z1i , ψ0 ) By Theorem 9.1, the solution to the estimating equation " n Ri m(Zi , β) Ri − π(Z1i , ψˆn ) − h∗2 (Z1i , β0 , ξ ∗ ) = 0, π(Z1i , ψˆn ) π(Z1i , ψˆn )

(10.20)

i=1

where notice that we take β = β0 and ﬁx ξ ∗ in h∗2 (·), is an AIPWCC estimator for β whose inﬂuence function is −1 ∂m(Z, β0 ) − E × ∂β T R − π(Z1 , ψ0 ) Rm(Z, β0 ) ∗ ∗ − h2 (Z1 , β0 , ξ ) − Π [ ]|Λψ . (10.21) π(Z1 , ψ0 ) π(Z1 , ψ0 ) Let us denote the estimator that solves (10.20) by βˆn∗ . In the following theorem and proof, we give a heuristic justiﬁcation to show that estimating ξ using ξˆn∗ will have a negligible eﬀect on the resulting estimator. That is, βˆn , the solution to (10.17), is asymptotically equivalent to βˆn∗ , the solution to (10.20). By so doing, we deduce that the adaptive estimator βˆn is a consistent, asymptotically normal estimator for β whose inﬂuence function is given by (10.21). Theorem 10.3.

P → 0. n1/2 (βˆn − βˆn∗ ) −

Proof. If we return to the proof of Theorem 9.1, we note that the asymptotic approximation given by (9.13), applied to the estimator βˆn , the solution to (10.17) would yield −1 ∂m(Z, β0 ) 1/2 ˆ n (βn − β0 ) = − E × ∂β T " n ˆn ) R m(Z , β ) − π(Z , ψ R i i 0 i 1i ∗ ∗ −1/2 ˆ ˆ n − h2 (Z1i , βn , ξn ) + op (1), π(Z1i , ψˆn ) π(Z1i , ψˆn ) i=1

(10.22) whereas, applied to βˆn∗ , the solution to (10.20) would yield

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

−1 ∂m(Z, β0 ) ∗ ˆ n (βn − β0 ) = − E × ∂β T " n Ri m(Zi , β0 ) Ri − π(Z1i , ψˆn ) −1/2 n − h∗2 (Z1i , β0 , ξ ∗ ) + op (1). π(Z1i , ψˆn ) π(Z1i , ψˆn ) 1/2

i=1

(10.23) Taking diﬀerences between (10.22) and (10.23), we obtain that " n Ri − π(Z1i , ψˆn ) 1/2 ˆ ∗ −1/2 ˆ n (βn − βn ) = n h∗2 (Z1i , β0 , ξ ∗ ) ˆ π(Z , ψ ) 1i n i=1 " n ˆn ) R − π(Z , ψ i 1i −1/2 ∗ ∗ ˆ ˆ −n h2 (Z1i , βn , ξn ) + op (1). π(Z1i , ψˆn ) i=1 The proof is complete if we can show that the term in the square brackets above converges in probability to zero. This follows by expanding " n Ri − π(Z1i , ψˆn ) −1/2 n h∗2 (Z1i , βˆn , ξˆn∗ ) π(Z1i , ψˆn ) i=1

about β0 and ξ ∗ , while keeping ψˆn ﬁxed, to obtain " n ˆn ) R − π(Z , ψ 1i i (10.24) h∗2 (Z1i , β0 , ξ ∗ ) n−1/2 ˆn ) π(Z , ψ 1i i=1 " n Ri − π(Z1i , ψˆn ) ∂h∗2 (Z1i , βn∗ , ξn∗ ) −1 n1/2 (βˆn − β0 ) (10.25) + n T ˆ ∂β π(Z , ψ ) 1i n i=1 " n Ri − π(Z1i , ψˆn ) ∂h∗2 (Z1i , βn∗ , ξn∗ ) −1 n1/2 (ξˆn∗ − ξ ∗ ), (10.26) + n ∗T ˆn ) ∂ξ π(Z , ψ 1i i=1 where βn∗ and ξn∗ are intermediate values between βˆn and β0 and ξˆn∗ and P P ξ ∗ , respectively. Let us consider (10.26). Since ψˆn − → ψ0 , βn∗ − → β0 , and ∗ P ∗ ξn − → ξ , then, under suitable regularity conditions, the sample average in equation (10.26) is " n Ri − π(Z1i , ψˆn ) ∂h∗2 (Z1i , βn∗ , ξn∗ ) P −1 n − → ∂ξ ∗T π(Z1i , ψˆn ) i=1 ∗ ∂h2 (Z1 , β0 , ξ ∗ ) R − π(Z1 , ψ0 ) . E π(Z1 , ψ0 ) ∂ξ∗T This last expectation can be shown to equal zero by a simple conditioning argument where we ﬁrst ﬁnd the conditional expectation given Z1 . Since,

10.2 Improving Eﬃciency with Two Levels of Missingness

233

under suitable regularity conditions, n1/2 (ξˆn∗ − ξ ∗ ) is bounded in probability, then a simple application of Slutsky’s theorem can be used to show that (10.26) converges in probability to zero. A similar argument can be used also to show that (10.25) converges in probability to zero. If, in addition, the model for the conditional distribution of Z2 given Z1 was correctly speciﬁed, then R − π(Z1 , ψ0 ) R − π(Z1 , ψ0 ) ∗ ∗ h2 (Z1 , β0 , ξ ) = h02 (Z1 , β0 ) π(Z1 , ψ0 ) π(Z1 , ψ0 ) Rm(Z, β0 ) Λ2 . =Π π(Z1 , ψ0 ) This would then imply that the term inside the square brackets “[ · ]”in (10.21) is an element orthogonal to Λ2 (i.e., [ · ] ∈ Λ⊥ 2 ), in which case Π [ · ] Λψ (i.e., the last term of (10.21)) is equal to zero because Λψ ⊂ Λ2 . The resulting estimator would have inﬂuence function RϕF (Z) RϕF (Z) −Π Λ2 = J (ϕF ) ∈ (IF )DR , (10.27) π(Z1 , ψ0 ) π(Z1 , ψ0 ) −1 0) where ϕF (Z) = − E ∂m(Z,β m(Z, β0 ), and this inﬂuence function is T ∂β within the class of the so-called double-robust inﬂuence functions. The variance of this inﬂuence function represents the smallest asymptotic variance among observed-data estimators that used m(Z, β0 ) = ϕ∗F (Z) as the basis for an augmented inverse probability weighted complete-case estimating equation. Estimating the Asymptotic Variance To estimate the asymptotic variance of βˆn , where βˆn is the solution to (10.17), we propose using the sandwich variance estimator given in Chapter 9, equation (9.19). For completeness, this estimator is given by " ˆn ) −1 ∂m(Z, β ˆn = E ˆ Σ ∂β T n −1 ∗ T ∗ ˆ ˆ ˆ ˆ ˆ ˆ × n g(Oi , ψn , βn , ξn )g (Oi , ψn , βn , ξn ) i=1

" T ˆn ) −1 ∂m(Z, β ˆ × E , ∂β T where

(10.28)

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

ˆ E

∂m(Z, βˆn ) ∂β T

" =n

−1

n i=1

Ri ∂m(Zi , βˆn )/∂β T π(Z1i , ψˆn )

" ,

g(Oi , ψˆn , βˆn , ξˆn∗ ) = T T −1 ˆ ˆ q(Oi , ψˆn , βˆn , ξˆ∗ ) − E(qS Sψ (Oi , ψˆn ), ψ ){E(Sψ Sψ )} n

Ri m(Zi , βˆn ) q(Oi , ψˆn , βˆn , ξˆn∗ ) = − π(Z1i , ψˆn ) T −1 ˆ E(qS ψ) = n

n

Ri − π(Z1i , ψˆn ) π(Z1i , ψˆn )

" h∗2 (Z1i , βˆn , ξˆn∗ ),

q(Oi , ψˆn , βˆn , ξˆn∗ )SψT (Oi , ψˆn ),

i=1

and ˆ ψ S T ) = n−1 E(S ψ

n

Sψ (Oi , ψˆn )SψT (Oi , ψˆn ).

i=1

Double Robustness with Two Levels of Missingness Thus far, we have taken the point of view that the missingness model was correctly speciﬁed; that is, that π0 (Z1 ) = P (R = 1|Z1 ) is contained in the model π(Z1 , ψ) for some value of ψ. If this is the case, we denote the true value of ψ by ψ0 and π0 (Z1 ) = π(Z1 , ψ0 ). However, unless the missingness was by design, we generally don’t know the true missingness model, and therefore the possibility exists that we have misspeciﬁed this model. Even if the missingness model is misspeciﬁed, under suitable regularity conditions, the maximum likelihood estimator ψˆn will converge in probability to some constant ψ ∗ , but π(Z1 , ψ ∗ ) = π0 (Z1 ). That is, π(Z1 , ψˆn ) → π(Z1 , ψ ∗ ) = π0 (Z1 ). As we will demonstrate, the attempt to gain eﬃciency by positing a model for the conditional distribution of Z2 given Z1 and estimating h∗2 (Z1 , β, ξˆn∗ ) = E{m(Z, β)|Z1 , ξ}

∗ ξ=ξˆn

actually gives us the extra protection of double robustness, which was brieﬂy introduced in Section 6.5. We now explore this issue further. Using standard asymptotic approximations, we can show that the estimator βˆn , which is the solution to the estimating equation (10.17), will be consistent and asymptotically normal if

10.2 Improving Eﬃciency with Two Levels of Missingness

E

Rm(Z, β0 ) − π(Z1 , ψ ∗ )

R − π(Z1 , ψ ∗ ) π(Z1 , ψ ∗ )

h∗2 (Z1 , β0 , ξ ∗ ) = 0,

235

(10.29)

P P where ψˆn → ψ ∗ and ξˆn∗ → ξ ∗ . In developing our estimator for β, we considered two models, one for the missingness probabilities π(Z1 , ψ) and another for the conditional density of Z2 given Z1 p∗Z2 |Z1 (z2 |z1 , ξ). If the missingness model is correctly speciﬁed, then π(Z1 , ψ ∗ ) = π(Z1 , ψ0 ) = P (R = 1|Z1 ). (10.30)

If the model for the conditional density of Z2 given Z1 is correctly speciﬁed, then h∗2 (Z1 , β0 , ξ ∗ ) = E0 {m(Z, β0 )|Z1 }. (10.31) We now show the so-called double-robustness property; that is, the estimator βˆn , the solution to (10.17), is consistent and asymptotically normal (a result that follows under suitable regularity conditions when (10.29) is satisﬁed) if either (10.30) or (10.31) is true. After adding and subtracting similar terms, we write the expectation in (10.29) as R − π(Z1 , ψ ∗ ) ∗ ∗ E m(Z, β0 ) + m(Z, β0 ) − h2 (Z1 , β0 , ξ ) π(Z1 , ψ ∗ ) R − π(Z1 , ψ ∗ ) ∗ ∗ m(Z, β0 ) − h2 (Z1 , β0 , ξ ) . = 0+E (10.32) π(Z1 , ψ ∗ ) If (10.30) is true, whether (10.31) holds or not, we write (10.32) as R − P (R = 1|Z1 ) ∗ ∗ m(Z, β0 ) − h2 (Z1 , β0 , ξ ) . E (10.33) P (R = 1|Z1 ) We derive the expectation of (10.33) by using the law of conditional iterated expectations, where we ﬁrst condition on Z = (Z1 , Z2 ) to obtain E(R|Z1 , Z2 ) − P (R = 1|Z1 ) ∗ ∗ m(Z, β0 ) − h2 (Z1 , β0 , ξ ) . (10.34) E P (R = 1|Z1 ) Because of MAR, R ⊥ ⊥ Z2 |Z1 , which implies that E(R|Z1 , Z2 ) = E(R|Z1 ) = P (R = 1|Z1 ). This then implies that (10.34) equals zero, which, in turn, implies that βˆn is consistent when (10.30) is true. If (10.31) is true, whether (10.30) holds or not, we write (10.32) as R − π(Z1 , ψ ∗ ) m(Z, β0 ) − E0 {m(Z, β0 )|Z1 } . E (10.35) π(Z1 , ψ ∗ ) Again, we evaluate (10.35) by using the law of conditional iterated expectations, where we ﬁrst condition on (R, Z1 ) to obtain

236

10 Improving Eﬃciency and Double Robustness with Coarsened Data

E

R − π(Z1 , ψ ∗ ) π(Z1 , ψ ∗ )

E{m(Z, β0 )|R, Z1 } − E0 {m(Z, β0 )|Z1 } . (10.36)

Because of MAR, R ⊥ ⊥ Z2 |Z1 , which implies that E{m(Z, β0 )|R, Z1 } = E0 {m(Z, β0 )|Z1 }. This then implies that (10.36) equals zero, which, in turn, implies that βˆn is consistent when (10.31) is true. Remarks Regarding Double-Robust Estimators In developing the adaptive strategy that led to double-robust estimators, we had to posit a simplifying model for p∗Z (z, ξ) or for p∗Z2 |Z1 (z2 |z1 , ξ) and then estimate the parameter ξ. We originally took the point of view that the model for the coarsening (missingness) probabilities π(Z1 , ψ) was correctly speciﬁed and hence the posited model for the distribution of Z was used to compute projections onto the augmentation space, which, in turn, gained us eﬃciency while still leading to a consistent and asymptotically normal estimator for β even if the posited model was misspeciﬁed. To estimate the parameter ξ, we suggested using likelihood methods such as maximizing (10.15) or (10.16). However, any estimator that would lead to a consistent estimator of ξ (assuming the posited model was correct) could have been used. In fact, we might use an IPWCC or AIPWCC estimator for ξ since, in some cases, these may be easier to implement. For example, if a full-data estimating function for ξ was easily obtained (i.e., m(Z, ξ) such that Eξ {m(Z, ξ)} = 0), then a simple IPWCC estimator for ξ, using observed data, could be obtained by solving n Ri m(Zi , ξ) i=1

π(Z1i , ψˆn )

= 0.

(10.37)

Such a strategy is perfectly reasonable as long as we believe that the model for the coarsening probabilities is correctly speciﬁed because this is necessary to guarantee that the solution to (10.37) would lead to a consistent estimator of ξ if the posited model for the distribution of Z was correct. However, if our goal is to obtain a double-robust estimator for β, then we must make sure that the estimator for ξ is a consistent estimator if the posited model for the distribution of Z was correctly speciﬁed, regardless of whether the model for the coarsening probabilities was correctly speciﬁed or not. This means that we could use likelihood methods such as maximizing (10.15) or (10.16), as such estimators do not involve the coarsening probabilities, but we could not use IPWCC or AIPWCC estimators such as (10.37). Logistic Regression Example Revisited In Chapter 7, we gave an example where we were interested in estimating the parameter β in a logistic regression model used to model the relationship of a binary outcome variable Y as a function of covariates X = (X1T , X2 )T ,

10.2 Improving Eﬃciency with Two Levels of Missingness

237

where, for ease of illustration, we let X2 be a single random variable. Also, for this example, we let all the covariates in X be continuous random variables. Speciﬁcally, we consider the model P (Y = 1|X) =

exp(β T X ∗ ) , 1 + exp(β T X ∗ )

where X ∗ = (1, X1T , X2 )T is introduced to allow for an intercept term. We also denote the full data Z = (Y, X). We considered the case where there were two levels of missingness, speciﬁcally where (Y, X1 ) was always observed but where the single variable X2 was missing on some of the individuals. The observed data are denoted by Oi = (Ri , Yi , X1i , Ri X2i ), i = 1, . . . , n. In the example in Chapter 7, we assumed that missingness was by design, but here we will assume that the missing data were not by design and hence the probability of missingness has to be modeled. We assume that the data are missing at random (MAR) and the missingness probability, or more precisely the probability of a complete case, follows the logistic regression model (10.8). π(Y, X1 , ψ) =

exp(ψ0 + ψ1 Y + ψ2T X1 ) . 1 + exp(ψ0 + ψ1 Y + ψ2T X1 )

(10.38)

Estimates of the parameter ψ = (ψ0 , ψ1 , ψ2T )T are obtained by maximizing the likelihood (10.9). This can be easily accomplished using standard statistical software. The resulting estimator is denoted by ψˆn and the estimator for the probability of a complete case is denoted by π(Yi , X1i , ψˆn ). We next consider the choice for the full-data estimating function m(Z, β). With full data, the optimal choice for m(Z, β) is exp(β T X ∗ ) . m(Y, X1 , X2 , β) = X ∗ Y − 1 + exp(β T X ∗ ) Although this may not be the optimal choice with the introduction of missing data, for the time being we will use this to construct observed-data AIPWCC estimators. In Chapter 7, AIPWCC estimators were introduced for this problem using equation (7.49) when the data were missing by design. When the missingness probability is modeled using (10.38), then we would consider AIPWCC estimators that are the solution to n

Xi∗ Yi −

exp(β T Xi∗ ) ˆ 1 + exp(β T Xi∗ ) i=1 π(Yi , X1i , ψn ) " Ri − π(Yi , X1i , ψˆn ) − L(Yi , X1i ) = 0, π(Yi , X1i , ψˆn ) Ri

(10.39)

where L(Y, X1 ) is some arbitrary q-dimensional function of Y and X1 . We now realize that in order to obtain as eﬃcient an estimator as possible

238

10 Improving Eﬃciency and Double Robustness with Coarsened Data

for β among the estimators in (10.39), we should choose L(Y, X1 ) to equal E{m(Y, X1 , X2 , β)|Y, X1 }; that is, exp(β T X ∗ ) L∗2 (Y, X1 ) = E X ∗ Y − Y, X . 1 1 + exp(β T X ∗ ) Because the function L(Y, X1 ) depends on the conditional distribution of X2 given Y and X1 , which is unknown to us, we posit a model for this conditional distribution and use adaptive methods. The model we consider is motivated by the realization that a logistic regression model for Y given X would be obtained if the conditional distribution of X given Y followed a multivariate normal distribution with a mean that depends on Y but with a variance matrix that is independent of Y ; that is, the conditional distribution of X given Y = 1 would be M V N (µ1 , Σ) and would be M V N (µ0 , Σ) given Y = 0; see, for example, Cox and Snell (1989). For this scenario, the conditional distribution of X2 given X1 and Y would follow a normal distribution; i.e., T (10.40) X2 |X1 , Y ∼ N ξ0 + ξ1 X1 + ξ2 Y, ξσ2 . Therefore, one strategy is to posit the model (10.40) for the conditional distribution of X2 given X1 and Y and estimate the parameters ξ = (ξ0 , ξ1T , ξ2 , ξσ2 )T by maximizing (10.16). This is especially attractive because the model (10.40) is a traditional normally distributed linear model. Therefore, using the complete cases (i.e., {i : Ri = 1}), the MLE for (ξ0 , ξ1T , ξ2 )T can be obtained using standard least squares and the MLE for the variance parameter ξσ2 can be obtained as the average of the squared residuals. Denote this estimator by ξˆn∗ . Finally, me must compute L∗2 (Y, X1 , β, ξˆn∗ ) = E{m(Y, X1 , X2 , β)|Y, X1 , ξ}|ξ=ξˆ∗ n $ # ∗ ∗T ∗ ˆ X1 + ξˆ2n Y )}2 {u − (ξ0n + ξˆ1n ∗ −1/2 ˆ du. = m(Y, X1 , u, β)(2π ξσn2 ) exp − 2ξˆσ∗ 2 n

This can be accomplished using numerical integration or Monte Carlo techniques. The estimator for β is then obtained by solving the equation exp(β T Xi∗ ) ˆ 1 + exp(β T Xi∗ ) i=1 π(Yi , X1i , ψn ) " Ri − π(Yi , X1i , ψˆn ) ∗ ∗ ˆ − L2 (Yi , X1i , β, ξn ) = 0. π(Yi , X1i , ψˆn )

n

Ri

Xi∗ Yi −

(10.41)

10.3 Improving Eﬃciency with Monotone Coarsening

239

This estimator is doubly robust; it is a consistent asymptotically normal RAL estimator for β if either the missingness model (10.38) or the model for the conditional distribution of X2 given X1 and Y (10.40) is correct. The asymptotic variance for βˆn can be obtained by using the sandwich variance estimator (10.28).

10.3 Improving Eﬃciency with Monotone Coarsening Finding the Projection onto the Augmentation Space Monotone missingness, or more generally monotone coarsening, occurs often in practice and is worth special consideration. We have shown that a natural way to obtain semiparametric coarsened-data estimators for β is to consider augmented inverse probability weighted complete-case estimators, estimators based on n I(Ci = ∞)m(Zi , β) (10.42) + L2 {Ci , GCi (Zi )} = 0, (∞, Zi , ψˆn ) i=1

where m(Zi , β) is an estimating function such that m(Z, β0 ) = ϕ∗F (Z) ∈ ΛF ⊥ and L2 {C, GC (Z)} ∈ Λ2. We also proved in Section 10.1 that we should choose L2 {C, GC (Z)} = −Π

I(C=∞)m(Z,β) (∞,Z,ψ)

Λ2 in order to gain the greatest

eﬃciency among estimators that solve (10.42); see (10.7). Therefore, to obtain estimators for β with improved eﬃciency, we now consider how to ﬁnd the projection of I(C=∞)m(Z,β) onto the augmentation space Λ2 when coarsening (∞,Z,ψ) is monotone. We remind the reader that a typical element of Λ2 , written in terms of the coarsening probabilities, is given by (7.37). However, under monotone coarsening, we showed (in Theorem 9.2) that a typical element of Λ2 can be reparametrized, in terms of discrete hazard functions, as I(C = r) − λr {Gr (Z)}I(C ≥ r) Lr {Gr (Z)}, Kr {Gr (Z)} r=∞

where the discrete hazard function λr {Gr (Z)} and Kr {Gr (Z)} are deﬁned by (8.2) and (8.4), respectively, and Lr {Gr (Z)} denotes an arbitrary function of Gr (Z) for r = ∞. By the projection theorem, if we want to ﬁnd I(C = ∞)m(Z, β) Λ2 , Π (∞, Z) then we must derive the functions L0r {Gr (Z)}, r = ∞, such that

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

% E

I(C = ∞)mT (Z, β) (∞, Z) & I(C = r) − λr {Gr (Z)}I(C ≥ r) T L0r {Gr (Z)} − Kr {Gr (Z)} r=∞ ⎛ ⎞ I(C = r) − λr {Gr (Z)}I(C ≥ r) Lr {Gr (Z)}⎠ ×⎝ Kr {Gr (Z)} r=∞

=0

for all Lr {Gr (Z)}, r = ∞.

(10.43)

onto Λ2 (i.e., the solution to Theorem 10.4. The projection of I(C=∞)m(Z,β) (∞,Z) (10.43)) is obtained by taking L0r {Gr (Z)} = −E{m(Z, β)|Gr (Z)}. Those readers who are familiar with the counting process notation and stochastic integral martingale processes that are used in an advanced course in survival analysis (see, for example, Fleming and Harrington, 1991) will ﬁnd that the proof of Theorem 10.4 uses similar methods. We ﬁrst derive some relationships in the following three lemmas that will simplify the calculations in (10.43). Lemma 10.1. For r = r I(C = r) − λr {Gr (Z)}I(C ≥ r) T L0r {Gr (Z)} E Kr {Gr (Z)} I(C = r ) − λr {Gr (Z)}I(C ≥ r ) Lr {Gr (Z)} = 0. (10.44) × Kr {Gr (Z)} Proof. For a single observation, deﬁne Fr to be the random vector {I(C = 1), I(C = 2), . . . , I(C = r − 1), Z}. Without loss of generality, take r > r. The expectation in (10.44) can be evaluated as E{E(·|Fr )}. Conditional on Fr , however, the only term in (10.44) that is not known is I(C = r ). Consequently, (10.44) can be written as E

But

I(C = r) − λr {Gr (Z)}I(C ≥ r) T L0r {Gr (Z)} Kr {Gr (Z)} E{I(C = r )|Fr } − λr {Gr (Z)}I(C ≥ r ) Lr {Gr (Z)} . × Kr {Gr (Z)} (10.45) E{I(C = r )|Fr } = P (C = r |C ≥ r , Z)I(C ≥ r ),

(10.46)

which by the coarsening at random assumption and the deﬁnition of a discrete hazard, given by (8.2), is equal to λr {Gr (Z)}I(C ≥ r ). Substituting (10.46) into (10.45) proves (10.44).

10.3 Improving Eﬃciency with Monotone Coarsening

Lemma 10.2. When r = r , the left-hand side of (10.44) equals I(C = r) − λr {Gr (Z)}I(C ≥ r) T L0r {Gr (Z)} E Kr {Gr (Z)} I(C = r) − λr {Gr (Z)}I(C ≥ r) Lr {Gr (Z)} × Kr {Gr (Z)} λr {Gr (Z)} T =E L {Gr (Z)}Lr {Gr (Z)} . Kr {Gr (Z)} 0r

241

(10.47)

Proof. Computing the expectation of the left-hand side of equation (10.47) by ﬁrst conditioning on Fr yields E[{I(C = r) − λr {Gr (Z)}I(C ≥ r)}2 |Fr ] T L0r {Gr (Z)}Lr {Gr (Z)} . E Kr2 {Gr (Z)} (10.48) The conditional expectation E[{I(C = r) − λr {Gr (Z)}I(C ≥ r)}2 |Fr ] is the conditional variance of the Bernoulli indicator I(C = r) given Fr , which equals λr {Gr (Z)}[1 − λr {Gr (Z)}]I(C ≥ r). Hence, (10.48) equals λr {Gr (Z)}[1 − λr {Gr (Z)}]I(C ≥ r) T L0r {Gr (Z)}Lr {Gr (Z)} . (10.49) E Kr2 {Gr (Z)} Computing the expectation of (10.49) by ﬁrst conditioning on Z gives λr (1 − λr )P (C ≥ r|Z) T E (10.50) L L 0r r . Kr2 Since P (C ≥ r|Z) =

r−1 .

(1 − λj ), we obtain that (1 − λr )P (C ≥ r|Z) = r . λr T , thus proving the (1 − λj ) = Kr . Therefore, (10.50) equals E K L L r 0r r j=1

j=1

lemma. Lemma 10.3. I(C = ∞)mT (Z, β) I(C = r) − λr {Gr (Z)}I(C ≥ r) E Lr {Gr (Z)} (∞, Z) Kr {Gr (Z)} λr {Gr (Z)} T = −E (10.51) m (Z, β)Lr {Gr (Z)} . Kr {Gr (Z)}

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Proof. Since I(C = ∞)I(C = r) = 0 for r = ∞ and I(R = ∞)I(C ≥ r) = I(C = ∞), the left-hand side of (10.51) equals I(C = ∞) λr {Gr (Z)} T m (Z, β)Lr {Gr (Z)} . −E (∞, Z) Kr {Gr (Z)} The result (10.51) holds by ﬁrst conditioning on Z and realizing that I(C = ∞) Z = 1. E (∞, Z) Proof. Theorem 10.4 Using the results of Lemmas 10.1–10.3, equation (10.43) can be written as

−

% E

r=∞

& T λr {Gr (Z)} m(Z, β) + L0r {Gr (Z)} Lr {Gr (Z)} = 0, (10.52) Kr {Gr (Z)}

for all functions Lr {Gr (Z)}, r = ∞. By conditioning each expectation in the sum on the left-hand side of (10.52) by Gr (Z), this can be written as λr {Gr (Z)} E{m(Z, β)|Gr (Z)} − E Kr {Gr (Z)} r=∞ T + L0r {Gr (Z)} Lr {Gr (Z)} = 0. (10.53) We now show that equation (10.53) holds if and only if L0r {Gr (Z)} = −E {m(Z, β)|Gr (Z)} for all r = ∞.

(10.54)

Clearly, (10.53) follows when (10.54) holds. Conversely, if (10.54) were not true, then we could choose Lr {Gr (Z)} = E{m(Z, β)| Gr (Z)} + L0r {Gr (Z)} for all r = ∞ to get a contradiction. Therefore, we have demonstrated that, with monotone CAR, I(C = ∞)m(Z, β) Λ2 Π (∞, Z) I(C = r) − λr {Gr (Z)}I(C ≥ r) =− E {m(Z, β)|Gr (Z)} . (10.55) Kr {Gr (Z)} r=∞

In order to take advantage of the results above, we need to compute E{m(Z, β)|Gr (Z)}. This requires us to estimate the distribution of Z, or at least enough of the distribution to be able to compute these conditional expectations.

10.3 Improving Eﬃciency with Monotone Coarsening

243

Remark 4. This last statement almost seems like circular reasoning. That is, we argue that to gain greater eﬃciency, we would need to estimate the distribution of Z. However, if we had methods to estimate the distribution of Z, then we wouldn’t need to develop this theory in the ﬁrst place. The rationale for considering semiparametric theory for coarsened data is that it led us to augmented inverse probability weighted complete-case estimators, which, we argued, build naturally on full-data estimators and are easier to derive than, say, likelihood methods with coarsened data. However, as we saw in the case with two levels of missingness, we will still obtain consistent asymptotically normal estimators using this inverse weighted methodology even if we construct estimators for the distribution of Z that are incorrect. This gives us greater ﬂexibility and robustness and suggests the use of an adaptive approach, as we now describe.

Adaptive Estimation To take advantage of the results for increased eﬃciency, we consider an adaptive approach. That is, we posit simpler models for the distribution of Z only for the purpose of approximating the conditional expectations E{m(Z, β)|Gr (Z)}, r = ∞. We do not necessarily expect that these posited models are correct, although we do hope that they may serve as a reasonable approximation. However, even if the posited model is incorrect, the resulting expectation, which we denote as EINC {m(Z, β)|Gr (Z)}, because EINC {m(Z, β)|Gr (Z)} = E0 {m(Z, β)|Gr (Z)} , still results in a function of Gr (Z). Consequently, computing (10.55) under incorrectly posited models would lead us to use L2 {C, GC (Z)} I(C = r) − λr {Gr (Z)}I(C ≥ r) EINC {m(Z, β)|Gr (Z)} = Kr {Gr (Z)}

(10.56)

r=∞

in the estimating equation (10.42). Even though (10.56) is not the correct proI(R = ∞)m(Z, β) jection of onto Λ2 , if the posited model for Z is incorrect, (∞, Z) it is still an element of the augmentation space Λ2 , which implies that the solution to (10.42), using the augmented term L2 (·) as given by (10.56), would still result in an AIPWCC consistent, asymptotically normal semiparametric estimator for β. This protection against misspeciﬁed models argues in favor of using an adaptive approach. In an adaptive strategy, to improve eﬃciency, we start by positing a simpler and possibly incorrect model for the distribution of the full data Z. Say we

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

assume Z ∼ p∗Z (z, ξ), where ξ is ﬁnite-dimensional. Under this presumed model, we could compute the conditional expectations E {m(Z, β)|Gr (Z), ξ} =

m(z, β)p∗Z (z, ξ)dνZ (z)

{Gr (z)=Gr (Z)}

p∗Z (z, ξ)dνZ (z)

.

(10.57)

{Gr (z)=Gr (Z)}

Of course, we need to estimate the parameter ξ in our posited model. Because the parameter ξ is ﬁnite-dimensional, we can estimate ξ using likelihood techniques as described in Section 7.1. Using (7.10), the likelihood for a realization of such data, (ri , gri ), i = 1, . . . , n, when the coarsening mechanism is CAR, is equal to n !

p∗Gr

i=1

i

(Zi ) (gri , ξ),

where p∗Gr (Z) (gr , ξ) =

(10.58)

p∗Z (z, ξ)dνZ (z).

{z:Gr (z)=gr }

Consequently, we would estimate E{m(Z, β)|Gr (Z), ξ} by substituting ξˆn∗ for ξ in (10.57), where ξˆn∗ is the MLE obtained by maximizing (10.58). The estimated conditional expectation is denoted by E{m(Z, β)|Gr (Z), ξˆn∗ }. Remark 5. Because of the monotone nature of the coarsening, it may be convenient to build models for the density of Z by considering models for the conditional density of Gr (Z) given Gr −1 (Z). Speciﬁcally, the density of Z can be written as PZ∗ (z) =

∞ ! r =1

p∗Gr (Z)|Gr −1 (Z) (gr |gr −1 ),

where gr = Gr (z), p∗G1 (Z)|G0 (Z) (g1 |g0 ) = p∗G1 (Z) (g1 ),

p∗Gr (Z)|Gr −1 (Z) (gr |gr −1 ) = p∗Z|G (Z) (z|g ) when r = ∞, and denotes the number of levels of coarsening. With this representation, we can write the density ! p∗Gr (Z)|Gr −1 (Z) (gr |gr −1 ). p∗Gr (Z) (gr ) = r ≤r

If, in addition, we consider models for the conditional density

10.3 Improving Eﬃciency with Monotone Coarsening

245

p∗Gr (Z)|Gr−1 (Z) (gr |gr−1 , ξr ), in terms of parameter ξr , where ξr , for diﬀerent r, are variationally independent, then the likelihood (10.58) can be written as ! ! p∗G1 (Zi ) (g1i , ξ1 ) p∗G2 (Zi )|G1 (Zi ) (g2i |g1i , ξ2 ) i:ri ≥1

× ... ×

i:ri ≥2

! i:ri =∞

p∗Zi |G (Zi ) (zi |gi , ξ∞ ).

(10.59)

The maximum likelihood estimator for ξ = (ξ1 , . . . , ξ∞ )T can then be obtained by maximizing each of the terms in (10.59) separately. Thus, the adaptive approach to ﬁnding estimators when data are monotonically coarsened is as follows. 1. We ﬁrst consider the full-data problem. That is, how would semiparametric estimators for β be obtained if we had full data? For example, we may use a full-data m-estimator for β, which is the solution to n

m(Zi , β) = 0,

i=1

which has inﬂuence function −1 ∂m(Zi , β0 ) − E m(Zi , β0 ) = ϕF (Zi ). ∂β T 2. Next, we consider the augmented inverse probability weighted completecase estimator that is the solution to (10.42), with L2 (·) being an estimator of (10.56). Speciﬁcally, we consider the estimator for β that solves the estimating equation % n I(Ci = ∞)m(Zi , β) + (∞, Zi , ψˆn ) i=1 # $ I(Ci = r) − λr {Gr (Zi ), ψˆn }I(Ci ≥ r) E{m(Z, β)|Gr (Zi ), ξˆn∗ } Kr {Gr (Zi ), ψˆn } r=∞ = 0, where (∞, Zi , ψˆn ) =

(10.60) .

ˆ 1 − λ {G (Z ), ψ } , (see (8.6)) ψˆn is the r r i n r<∞

maximum likelihood estimator for ψ obtained by maximizing (8.12), and E{m(Z, β)|Gr (Zi ), ξˆn∗ } is obtained using (10.57), substituting ξˆn∗ , which maximizes (10.58) or (10.59), for ξ. We denote this estimator by βˆn .

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Even though the posited model p∗Z (z, ξ) may not be correctly speciﬁed, under suitable regularity conditions, the estimator ξˆn∗ will converge in probability to a constant ξ ∗ and that n1/2 (ξˆn∗ −ξ ∗ ) will be bounded in probability. Also, even if the posited model is incorrect, the function L2 {Ci , GCi (Zi ), β, ψ0 , ξ ∗ } ∈ Λ2 , where L2 {Ci , GCi (Zi ), β, ψ, ξ} = I(Ci = r) − λr {Gr (Zi ), ψ}I(Ci ≥ r) E{m(Z, β)|Gr (Zi ), ξ}. Kr {Gr (Zi ), ψ} r=∞

Consequently, the solution to the estimating equation # $ n I(Ci = ∞)m(Zi , β) + L2 {Ci , GCi (Zi ), β0 , ψˆn , ξ ∗ } = 0, (∞, Zi , ψˆn )

(10.61)

i=1

with β set to β0 and ξ ∗ ﬁxed in L2 (·), is an AIPWCC estimator for β, which we denote by βˆn∗ . Using an argument similar to that in Theorem 10.3, when we considered two levels of missingness, we can show that, under suitable regularity conditions, the estimator βˆn , which solves equation (10.60), is asymptotically equivalent to the AIPWCC estimator βˆn∗ , which solves (10.61); that is, P n1/2 (βˆn − βˆn∗ ) → 0.

(We leave this as an exercise for the reader.) The resulting inﬂuence function for βˆn , which is the same as for βˆn∗ , can now be derived by Theorem 9.1 and is equal to I(Ci = ∞)ϕF (Zi ) + L∗2 {Ci , GCi (Zi ), β0 , ψ0 , ξ ∗ } (∞, Zi , ψ0 ) I(Ci = ∞)ϕF (Zi ) , −Π + L∗2 {Ci , GCi (Zi ), β0 , ψ0 , ξ ∗ } Λψ (∞, Zi , ψ0 ) where

−1 ∂m(Zi , β0 ) m(Zi , β0 ) ϕ (Zi ) = E ∂β T F

and L∗2 {Ci , GCi , β0 , ψ0 , ξ ∗ } = −1 ∂m(Zi , β0 ) − E L2 {Ci , GCi (Zi ), β0 , ψ0 , ξ ∗ }. ∂β T The asymptotic variance of βˆn can also be obtained by using the sandwich variance estimator for AIPWCC estimators given by (9.19).

10.3 Improving Eﬃciency with Monotone Coarsening

247

Remark 6. If the posited model p∗ (z, ξ) is correctly speciﬁed, then E{m(Zi , β0 )|Gr (Zi ), ξˆn∗ } will be a consistent estimator of E0 {m(Zi , β0 )|Gr (Zi )}, the true conditional expectation. In this case, L2 {Ci , GCi (Zi ), β0 , ψˆn , ξˆn∗ } will converge to

Π

I(Ci = ∞)m(Zi , β0 ) Λ2 . (∞, Zi , ψ0 )

For the case of a correctly speciﬁed model, the inﬂuence function is I(Ci = ∞)ϕF (Zi ) I(Ci = ∞)ϕF (Zi ) −Π Λ2 (∞, Zi , ψ0 ) (∞, Zi , ψ0 ) −Π · Λψ . ⇑ This is orthogonal to Λ2 and since Λψ ⊂ Λ2 must equal 0 In this case, the inﬂuence function equals I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) −Π Λ2 = J {ϕF (Z)}, (∞, Z, ψ0 ) (∞, Z, ψ0 ) which is the most eﬃcient among observed-data inﬂuence functions associated with the full-data inﬂuence function ϕF (Zi ) and an element of (IF )DR . Generally, the attempt to estimate I(Ci = ∞)ϕF (Zi ) Λ2 Π (∞, Zi ) by positing a model p∗Z (z, ξ) often leads to more eﬃcient estimators even if the model was incorrect. In fact, this attempt to gain eﬃciency also gives us the extra protection of double robustness similar to that seen in the previous section when we considered two levels of missingness. We now explore this double-robustness relationship for the case of monotone coarsening.

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Double Robustness with Monotone Coarsening Throughout this section, we have taken the point of view that the model for the coarsening probabilities was correctly speciﬁed. That is, for some ψ0 , λr {Gr (Z), ψ0 } = P0 (C = r|C ≥ r, Z), r = ∞, where P0 (C = r|C ≥ r, Z) denotes the true discrete hazard rate. In actuality, this model may also be misspeciﬁed. Nonetheless, under suitable regularity conditions, the maximum likelihood estimator ψˆn , which maximizes (8.12), will converge to a constant ψ ∗ even if the model for the coarsening hazards is not correctly speciﬁed. When we developed the adaptive estimators for the purpose of improving eﬃciency, we considered the posited model p∗Z (z, ξ) and argued that the estimator ξˆn∗ converged to a constant ξ ∗ , where p∗Z (z, ξ ∗ ) may not be the correct distribution for the full data Z (i.e., p∗Z (z, ξ ∗ ) = p0Z (z)), where p0Z (z) = pZ (z, β0 , η0 ) denotes the true density of Z. We now consider the double-robustness property of the proposed adaptive estimator βˆn for β, the solution to (10.60). That is, we will prove that βˆn is a consistent estimator if either the model for λr {Gr (Z), ψ}, r = ∞ or the posited model p∗Z (z, ξ) is correctly speciﬁed. Using standard asymptotic arguments, the estimator βˆn will be consistent and asymptotically normal if we can show that I(C = ∞)m(Z, β0 ) + E (∞, Z, ψ ∗ ) I(C = r) − λr {Gr (Z), ψ ∗ }I(C ≥ r) ∗ E{m(Z, β0 )|Gr (Z), ξ } = 0. Kr {Gr (Z), ψ ∗ } r=∞

(10.62) It will be convenient to show ﬁrst that the expression inside the expectation on the left-hand side of (10.62) can be written as I(C = r) − λr {Gr (Z), ψ ∗ }I(C ≥ r) m(Z, β0 ) − Kr {Gr (Z), ψ ∗ } r=∞ × m(Z, β0 ) − E{m(Z, β0 )|Gr (Z), ξ ∗ } . (10.63) This follows because I(C = ∞)m(Z, β0 ) = m(Z, β0 ) + (∞, Z, ψ ∗ ) and by the following lemma.

I(C = ∞) − 1 m(Z, β0 ) (∞, Z, ψ ∗ )

(10.64)

10.3 Improving Eﬃciency with Monotone Coarsening

249

Lemma 10.4. I(C = r) − λr {Gr (Z), ψ ∗ }I(C ≥ r) I(C = ∞) = 1 − . (10.65) Kr {Gr (Z), ψ ∗ } (∞, Z, ψ ∗ ) r=∞

Proof. Lemma 10.4 Because of the discreteness of C, we can write r=∞

I(C = ∞) I(C = r) = . ∗ Kr {Gr (Z), ψ } KC {GC (Z), ψ ∗ }

(10.66)

By the deﬁnitions of λr (·) and Kr (·) given by (8.2) and (8.4), respectively, we obtain that λr (·) 1 1 = − , Kr (·) Kr (·) Kr−1 (·) where K0 (·) = 1. Consequently, −

λr (·)I(C ≥ r) 1 1 =I(C = ∞) − Kr (·) Kr−1 (·) Kr (·) r≤C r=∞ 1 1 + I(C = ∞) − Kr−1 (·) Kr (·) r=∞ 1 =I(C = ∞) 1 − KC {GC (Z), ψ ∗ } 1 + I(C = ∞) 1 − , K {G (Z), ψ ∗ }

(10.67)

where denotes the number of diﬀerent coarsening levels (i.e., the largest integer r < ∞) and ! K {G (Z), ψ ∗ } = [1 − λr {Gr (Z), ψ ∗ }] = (∞, Z, ψ ∗ ). (10.68) r=∞

Taking the sum of (10.66) and (10.67) and substituting (∞, Z, ψ ∗ ) for K {G (Z), ψ ∗ } (see (10.68)), we obtain I(C = ∞) 1 = 1 − , I(C = ∞) + I(C = ∞) 1 − (∞, Z, ψ ∗ ) (∞, Z, ψ ∗ ) thus proving the lemma. Therefore, to prove the double-robustness property of βˆn , it suﬃces to show that the expected value of (10.63) is equal to zero if either the model for λr {Gr (Z), ψ}, r = ∞ or the posited model p∗Z (z, ξ) is correctly speciﬁed, which we give by the following theorem.

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Theorem 10.5. I(C = r) − λr {Gr (Z), ψ ∗ }I(C ≥ r) E m(Z, β0 ) − Kr {Gr (Z), ψ ∗ } r=∞ × m(Z, β0 ) − E{m(Z, β0 )|Gr (Z), ξ ∗ } =0 if either the model for λr {Gr (Z), ψ}, r = ∞ or the posited model p∗Z (z, ξ) is correctly speciﬁed. Proof. By construction, E{m(Z, β0 )} = 0. Therefore, to prove Theorem 10.5, we must show that I(C = r) − λr {Gr (Z), ψ ∗ }I(C ≥ r) E Kr {Gr (Z), ψ ∗ } ∗ m(Z, β0 ) − E{m(Z, β0 )|Gr (Z), ξ } = 0, (10.69) for all r = ∞, if either the model for λr {Gr (Z), ψ}, r = ∞ or the posited model p∗Z (z, ξ) is correctly speciﬁed. We ﬁrst consider the case when the model for the coarsening probabilities is correctly speciﬁed (i.e., λr {Gr (Z), ψ ∗ } = λr {Gr (Z)} = P0 (C = r|C ≥ r, Z)), whether the posited model p∗Z (z, ξ) is correct or not. Deﬁning the random vector Fr = {I(C = 1), . . . , I(C = r − 1), Z}, as we did in the proof of Lemma 10.1, and deriving the expectation of (10.69) by ﬁrst conditioning on Fr , we obtain % E{I(C = r)|Fr } − λr {Gr (Z)}I(C ≥ r) E Kr {Gr (Z)} & ∗ × m(Z, β0 ) − E{m(Z, β0 )|Gr (Z), ξ } . We showed in (10.46) that E{I(C = r)|Fr } = λr {Gr (Z)}I(C ≥ r), which proves that (10.69) is equal to zero for all r = ∞ when the coarsening probabilities are modeled correctly, which, in turn, proves (10.62). Now, let’s consider the case when the posited model for the distribution of Z is correctly speciﬁed (i.e., p∗Z (z, ξ ∗ ) = p0Z (z)), whether or not the model for the coarsening probabilities is correct. If this model is correctly speciﬁed, then the conditional expectation E{m(Z, β0 )|Gr (Z), ξ ∗ } = E0 {m(Z, β0 )|Gr (Z)}. Write the expectation (10.69) as the diﬀerence of two expectations, namely I(C = r) m(Z, β0 ) − E0 {m(Z, β0 )|Gr (Z)} E (10.70) Kr {Gr (Z), ψ ∗ } λr {Gr (Z), ψ ∗ }I(C ≥ r) m(Z, β0 ) − E0 {m(Z, β0 )|Gr (Z)} . (10.71) −E Kr {Gr (Z), ψ ∗ }

10.3 Improving Eﬃciency with Monotone Coarsening

251

We compute the expectation in (10.71) by ﬁrst conditioning on {I(C ≥ r), Gr (Z)} to obtain λr {Gr (Z), ψ ∗ } E E{I(C ≥ r)m(Z, β0 )|I(C ≥ r), Gr (Z)} Kr {Gr (Z), ψ ∗ } − I(C ≥ r)E0 {m(Z, β0 )|Gr (Z)} . (10.72) But E{I(C ≥ r)m(Z, β0 )|I(C ≥ r), Gr (Z)} = I(C ≥ r)E{m(Z, β0 )|C ≥ r, Gr (Z)}. (10.73) Because of the coarsening at random (CAR) assumption, we obtain that pZ|C≥r,Gr (Z) (z|gr ) = pZ|Gr (Z) (z|gr ).

(10.74)

This follows because pZ|C≥r,Gr (Z) (z|gr ) =

P (C ≥ r|Z = z)p0Z (z) P (C ≥ r|Z = z)p0Z (z)dνZ (z) z:Gr (z)=gr

=

Kr {Gr (z)}p0Z (z) Kr {Gr (z)}p0Z (z)dνZ (z) z:Gr (z)=gr

=

p0Z (z) = pZ|Gr (Z) (z|gr ). p (z)dνZ (z) z:Gr (z)=gr 0Z

Equation (10.74), together with (10.73), implies that E{I(C ≥ r)m(Z, β0 )|I(C ≥ r), Gr (Z)} = I(C ≥ r)E0 {m(Z, β0 )|Gr (Z)} and hence (10.72) and (10.71) are equal to zero. A similar argument, where we condition on {I(C = r), Gr (Z)}, can be used to show that (10.70) is equal to zero. This then implies that (10.69) is equal to zero, which, in turn, implies that (10.62) is true, thus demonstrating that βˆn is a consistent estimator for β when the posited model p∗Z (z, ξ) is correctly speciﬁed.

Example with Longitudinal Data We return to Example 1, given in Section 9.2, where the interest was in estimating parameters that described the mean CD4 count over time, as a function of treatment, in a randomized study where CD4 counts were measured longitudinally at ﬁxed time points. Speciﬁcally, we considered two treatments: (X = 1) was the new treatment and (X = 0) was the control treatment. The response Y = (Y1 , . . . , Yl )T was a vector of CD4 counts that were measured on each individual at times 0 = t1 < . . . < tl . The full data are denoted by Z = (Y, X). It was assumed that CD4 counts follow a linear trajectory whose

252

10 Improving Eﬃciency and Double Robustness with Coarsened Data

slope may be treatment-dependent. Thus the model was given by (9.24) and assumes that E(Yji |Xi ) = β1 + β2 tj + β3 Xi tj . Therefore, the problem was to estimate the parameter β = (β1 , β2 , β3 )T from a sample of data Zi = (Yi , Xi ), i = 1, . . . , n, where Yi = (Y1i , . . . , Yli )T are the longitudinally measured CD4 counts for subject i. In this study, some patients dropped out, and for those patients we observed the CD4 count data prior to dropout, whereas all subsequent CD4 counts are missing. This is an example of monotone coarsening with = l − 1 levels of coarsening. We introduce the notation Y r to denote the vector of data (Y1 , . . . , Yr )T , r = 1, . . . , l and Y r¯ to denote the vector of data (Yr+1 , . . . , Yl )T , r = 1, . . . , l − 1. Therefore, when the coarsening variable is Ci = r, we observe Gr (Zi ) = (Xi , Yir ), r = 1, . . . , l − 1, and, when Ci = ∞, we observe the complete data G∞ (Zi ) = Zi = (Xi , Yi ). With such coarsened data {Ci , GCi (Zi )}, i = 1, . . . , n, we considered estimating the parameter β using an AIPWCC estimator. To accommodate this, we introduced a logistic regression model for the discrete hazard of coarsening probabilities given by (9.25) in terms of a parameter ψ, which was estimated by maximizing the likelihood (8.13). The resulting estimator was denoted by ψˆn . An AIPWCC estimator for β was proposed by solving the estimating equation (9.26), where, for simplicity, we chose m(Z, β) = H T (X){Y − H(X)β}. The deﬁnition of the design matrix H(X) is given subsequent to (9.24), and the rationale for this estimating equation is given in Section 9.2. Notice, however, that we deﬁned the augmentation term in equation (9.26) generically using arbitrary functions Lr {Gr (Z)}, r = ∞. We now know that to improve eﬃciency as much as possible, we should choose {Lr {Gr (Z)} = Em(Z, β)|Gr (Z)}, which requires adaptive estimation using a posited model for p∗Z (z, ξ). We propose the following. Assume that the distribution of Y given X follows a multivariate normal distribution whose mean and variance matrix may depend on treatment X; that is, Y |X = 1 ∼ M V N (ξ1 , Σ1 ) and Y |X = 0 ∼ M V N (ξ0 , Σ0 ), where ξk is an l-dimensional vector and Σk is an l × l matrix for k = 0, 1. The parameter ξ will denote (ξ0 , ξ1 , Σ0 , Σ1 ). Remark 7. Even though our model puts restrictions on the mean vectors ξk , in terms of the parameter β, we will let these be unrestricted and, as it turns out, these parameters will not come into play in our estimating equation.

10.3 Improving Eﬃciency with Monotone Coarsening

253

We denote ξkr = (ξ1k , . . . , ξrk )T and ξkr¯ = (ξ(r+1)k , . . . , ξlk )T for r = 1, . . . , l −1 and k = 0, 1. We also denote the corresponding elements of the partitioned r¯ r r¯r¯ r matrix for Σk by Σrr k , Σk , and Σk to represent the variance matrix of Y , the r r¯ r¯ covariance matrix of Y and Y , and the variance matrix of Y , respectively, given X = k. An estimator for ξ can be obtained by maximizing the likelihood n ! 1 l−1 ! ! −1/2 {(2π)r |Σrr k |} i=1 k=0

r=1

I(Ci =r,Xi =k) 1 −1 r r × exp − (Yir − ξkr )T (Σrr ) (Y − ξ ) k i k 2 I(Ci =∞,Xi =k) 1 l −1/2 T −1 . × {(2π) |Σk |} exp − (Yi − ξk ) Σk (Yi − ξk ) 2 (10.75)

This likelihood can be maximized by using standard statistical software such as SAS Proc Mixed; see Littell et al. (1996). Denote the estimators for the ˆ kn for k = 0, 1. Using standard results for the conditional variance matrix by Σ distribution of a multivariate normal distribution, we obtain that E{m(Z, β)|Gr (Z), ξ} = E[H T (X){Y − H(X)β}|X, Y r , ξ] = H T (X)q(r, X, Y r , β, ξ), where q(r, X, Y r , β, ξ) is an l-dimensional vector whose ﬁrst r elements are {Y r − H r (X)β}, whose last l − r elements are −1 (Σkr¯r )T (Σrr {Y r − H r (X)β}, when X = k, k = 0, 1, k )

and H r (X) is an r × 3 matrix consisting of the ﬁrst r rows of H(X). Therefore, the improved double-robust estimator for β is obtained by solving the equation n I(Ci = ∞)H T (Xi ){Yi − H(Xi )β} (∞, Zi , ψˆn ) i=1 " l−1 I(Ci = r) − λr {Gr (Zi ), ψˆn }I(Ci ≥ r) + Kr {Gr (Zi ), ψˆn } r=1

× H T (Xi )q(r, Xi , Yir , β, ξˆn∗ ) = 0,

ˆ kn , k = 0, 1, where, for this example, ξˆn∗ corresponds to the estimators, Σ derived by maximizing the likelihood (10.75).

254

10 Improving Eﬃciency and Double Robustness with Coarsened Data

10.4 Remarks Regarding Right Censoring In Section 9.3, we showed that right censoring, which occurs frequently in survival analysis, can be viewed as a special case of monotone coarsening with continuous-time hazard rates representing the distribution of censoring. We remind the reader that censored-data estimators for the parameter β can also be written as augmented inverse probability weighted complete-case estimators. Speciﬁcally, we argued in (9.34) that estimators for β can be derived by solving the estimating equation n i=1

∆i ¯ i (Ui )} m(Zi , β) + KUi {X

∞ 0

¯ i (r)} dMC˜i {r, X ¯ { X (r)} = 0, L r i ¯ i (r)} Kr {X

(10.76)

¯ )}), and m(Z, β) denotes a where Z denotes the full-data (i.e., Z = {T, X(T full-data estimating function that would have been used to obtain estimators ¯ for β had there been no censoring. The deﬁnition of dNC˜ (r), λC˜ {r, X(r)}, ¯ ¯ ¯ Y (r), dMC˜ {r, X(r)} = dNC˜ (r) − λC˜ {r, X(r)}Y (r)dr, and Kr {X(r)} were all deﬁned in Section 9.3. By analogy between the censored-data estimating equation (10.76) and the monotonically coarsened data estimating equation (10.42), we can show that the most eﬃcient augmented inverse probability weighted complete-case estimator for β that uses the full-data estimating function m(Z, β) is obtained by choosing ¯ ¯ Lr {X(r)} = E{m(Z, β)|T ≥ r, X(r)}. To actually implement these methods with censored data, we need to ¯ 1. develop models for the censoring distribution λC˜ {r, X(r), ψ} and ﬁnd estimators for ψ, and ¯ 2. estimate the conditional expectation E{m(Z, β)|T ≥ r, X(r)}. ¯ A popular model for the censoring hazard function λC˜ {r, X(r), ψ} is the semiparametric proportional hazards regression model (Cox, 1972) using maximum partial likelihood estimators to estimate the regression parameters and Breslow’s (1974) estimator to estimate the underlying cumulative hazard function. ¯ In order to estimate the conditional expectation E{m(Z, β)|T ≥ r, X(r)}, we can posit a simpler full-data model, say p∗Z (z, ξ) = p∗T,X(T {t, x ¯ (t), ξ}, ¯ ) ¯ and then estimate ξ using the observed data {Ui , ∆i , Xi (Ui )}, i = 1, . . . , n by maximizing the observed-data likelihood ∆i n ! ∗ ¯ pT,X(T × ¯ ) {Ui , Xi (Ui ), ξ} i=1

{t,¯ x(t)}:t≥Ui ,{x(s)=Xi (s),s≤Ui }

p∗T,X(T ¯(t), ξ}dνT,X(T ¯(t)} ¯ ) {t, x ¯ ) {t, x

1−∆i . (10.77)

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

255

Building models for p∗T,X(T ¯(t), ξ} with time-dependent covariate and ¯ ) {t, x maximizing (10.77) can be a daunting task. Nonetheless, the theory that has been developed can often be useful in developing more eﬃcient estimators even if we don’t necessarily derive the most eﬃcient one. In the example of censored medical cost data that was described in Example 2 of Section 9.3, Bang and Tsiatis (2000) used augmented inverse probability weighted complete-case estimators to estimate the mean medical cost and showed various methods for gaining eﬃciency by judiciously choosing the augmented term. Other examples where this methodology was used include Robins, Rotnitzky, and Bonetti (2001), who used AIPWCC estimators of the survival distribution under double sampling with follow-up of dropouts. Hu and Tsiatis (1996) and van der Laan and Hubbard (1998) constructed estimators of the survival distribution from survival data that are subject to reporting delays. Zhao and Tsiatis (1997, 1999, 2000) and van der Laan and Hubbard (1999) derived estimators of the quality-adjusted-lifetime distribution from right-censored data. Bang and Tsiatis (2002) derived estimators for the parameters in a median regression model of right-censored medical costs. Strawderman (2000) used these methods to derive an estimator of the mean of an increasing stopped stochastic process. Van der Laan, Hubbard, and Robins (2002) and Quale, van der Laan and Robins (2003) constructed locally eﬃcient estimators of a multivariate survival distribution when failure times are subject to a common censoring time and to failure-time-speciﬁc censoring.

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone We have discussed how to derive AIPWCC estimators with improved eﬃciency when there are two levels of coarsening or when the coarsening is monotone and have given several examples to illustrate these methods. This theory can also be extended to the case when the coarsening is nonmonotone. However, we must caution the reader that the use of AIPWCC estimators in this setting is very diﬃcult to implement. At the end of Section 8.1, we already remarked that developing coherent models for the missingness probabilities when the missingness is nonmonotone, is not trivial. There has been very little work in this area, with the exception of the paper by Robins and Gill (1997). Even if one were able to develop models for the missingness probabilities, ﬁnding projections onto the augmentation space, as is necessary to obtain more eﬃcient AIPWCC estimators, is not straightforward and requires an iterative process that is numerically diﬃcult to implement. Consequently, the semiparametric theory that leads to AIPWCC estimators has not been well developed with nonmonotone coarsened data and there is still a great deal of research that needs to be done in this area. Nonetheless, many of the theoretical results

256

10 Improving Eﬃciency and Double Robustness with Coarsened Data

have been worked out for nonmonotone coarsened data using the general theory developed by Robins, Rotnitzky, and Zhao (1994). For completeness, we present these results in this section, but again we caution the reader that there are many challenges yet to be tackled before these methods can be feasibly implemented. Finding the Projection onto the Augmentation Space We have already argued that among all coarsened-data inﬂuence functions given by (10.1) with ϕF (Z) ﬁxed, the optimal choice is given by (10.2). We ∗F (Z) have also shown how to ﬁnd the projection of I(C=∞)ϕ onto the aug(∞,Z) mentation space, Λ2 , when there are two levels of coarsening or when the coarsening is monotone, where ϕ∗F (Z) ∈ ΛF ⊥ . We now consider how to de∗F (Z) rive the projection of I(C=∞)ϕ onto Λ2 in general; that is, even if the (∞,Z) coarsening is nonmonotone. We begin by deﬁning two linear operators. Deﬁnition 4. The linear operator L is a mapping from the full-data Hilbert space HF to the observed-data Hilbert space H, where L : HF → H is deﬁned as L{hF (·)} = E{hF (Z)|C, GC (Z)}

(10.78)

for h (Z) ∈ H . Speciﬁcally, F

F

L{hF (·)} =

∞

I(C = r)E{hF (Z)|C = r, Gr (Z)},

r=1

and because of the coarsening-at-random assumption, we obtain L{hF (Z)} =

∞

I(C = r)E{hF (Z)|Gr (Z)}.

(10.79)

r=1

Deﬁnition 5. The linear operator M is a mapping from the full-data Hilbert space to the full-data Hilbert space. Speciﬁcally, M : HF → HF is deﬁned as M{hF (·)} = E[L{hF (·)}|Z].

(10.80)

Using (10.79), we obtain M{h (·)} = E F

∞

F

I(C = r)E{h (Z)|Gr (Z)}|Z

r=1

=

∞ r=1

{r, Gr (Z)}E{hF (Z)|Gr (Z)}.

(10.81)

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

The projection of orem.

I(C=∞)hF (Z) (∞,Z)

257

onto Λ2 is now given by the following the-

Theorem 10.6. (i) The inverse mapping M−1 exists and is uniquely deﬁned. (ii) The projection is

I(C = ∞)hF (Z) I(C = ∞)hF (Z) Π Λ2 = − L[M−1 {hF (·)}]. (∞, Z) (∞, Z)

(10.82)

Proof. Theorem 10.6 part (i) We will defer the proof of (i) and assume for the time being that it is true. Proof of Theorem 10.6 part (ii) If we can show that F

(Z) a. I(C=∞)h − L[M−1 {hF (·)}] ∈ Λ2 (∞,Z) b. and that I(C = ∞)hF (Z) I(C = ∞)hF (Z) − − L[M−1 {hF (·)}] (∞, Z) (∞, Z)

= L[M−1 {hF (·)}] is orthogonal to every element in Λ2 , then, by the projection theorem,

I(C=∞)hF (Z) (∞,Z)

− L[M−1 {hF (·)}] is the unique

F

(Z) onto Λ2 . projection of I(C=∞)h (∞,Z) We ﬁrst note that I(C = ∞)hF (Z) E − L[M−1 {hF (·)}] Z (∞, Z)

= hF (Z) − M[M−1 {hF (·)}] = hF (Z) − hF (Z) = 0, thus proving (a). If we let L2 {C, GC (Z)} be an arbitrary element of Λ2 , then the inner product

258

10 Improving Eﬃciency and Double Robustness with Coarsened Data

T L[M−1 {hF (·)}] L2 {C, GC (Z)} T = E E M−1 {hF (·)} L2 {C, GC (Z)} C, GC (Z) T = E M−1 {hF (·)} L2 {C, GC (Z)} −1 F T = E E M {h (·)} L2 {C, GC (Z)} Z −1 F T = E M {h (·)} E L2 {C, GC (Z)} Z = 0,

E

where the last equality follows because M−1 (hF ) ∈ HF and hence as a function of Z, allowing it to come outside the inner conditional expectation, and L2 (·) ∈ Λ2 , which implies that E{L2 (·)|Z} = 0, thus proving (b). Uniqueness of M−1 (·) In order to complete the proof of Theorem 10.6, we must show that the linear operator M has a unique inverse. We will prove the existence and uniqueness of the inverse of the linear mapping M by showing that the linear operator (I − M) is a contraction mapping, where I denotes the identity mapping HF → HF ; i.e., I{hF (Z)} = hF (Z) for hF ∈ HF . For more details on these methods, we refer the reader to Kress (1989). We begin by ﬁrst deﬁning what we mean by a contraction mapping and proving why (I − M) being a contraction mapping implies that M has a unique inverse. Deﬁnition 6. A linear operator, say (I − M), is a contraction mapping if the norm of the operator satisﬁes I − M ≤ (1 − ε) for some ε > 0, where the norm of a linear operator, say I − M , is deﬁned as suphF ∈HF

(I − M)(hF ) ,

hF

or equivalently, I − M ≤ (1 − ε), if

(I − M)(hF ) ≤ (1 − ε) hF

for all hF ∈ HF .

Lemma 10.5. If (I − M) is a contraction mapping, then M−1 exists, is unique, and is equal to the operator S=

∞

(I − M)k .

(10.83)

k=0

Also, M−1 {hF (Z)} can be obtained by successive approximation, where

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

ϕn+1 (Z) = (I − M)ϕn (Z) + hF (Z);

259

(10.84)

i.e., ϕ0 (Z) = hF (Z) ϕ1 (Z) = (I − M)hF (Z) + hF (Z) ϕ2 (Z) = (I − M)2 hF (Z) + (I − M)hF (Z) + hF (Z) .. . ϕn (Z) → M−1 {hF (·)}.

and

Proof. To demonstrate existence, we must show M[S{hF (Z)}] = hF (Z). However, ∞ " M S{hF (Z)} = M (I − M)k hF (Z) k=0

= {I − (I − M)}

∞

" (I − M) h (Z) . k F

k=0

By a telescoping argument, this equals ' ( lim I − (I − M)k hF (Z), k→∞

but (I − M)k hF (Z) will have a norm that converges to zero as k → ∞. This follows because (I − M) is a contraction mapping and (I − M)k hF ≤ (1 − )k hF . Therefore lim (I − M)k hF (Z) = 0 a.s. Consequently, k→∞

M[S{hF (Z)}] = hF (Z). We will demonstrate uniqueness by contradiction. Suppose M−1 (·) were not unique. Then there exists S ∗ {hF (Z)} such that M[S ∗ {hF (Z)}] = hF (Z)

but

S ∗ {hF (Z)} = S{hF (Z)}. In that case, (I − M)(S − S ∗ )hF (Z) = (I − M)[S{hF (Z)} − S ∗ {hF (Z)}] = S{hF (Z)} − S ∗ {hF (Z)} and

260

10 Improving Eﬃciency and Double Robustness with Coarsened Data

(I − M)[S{hF (Z)} − S ∗ {hF (Z)}] = S{hF (Z)} − S ∗ {hF (Z)} . But since (I − M) is a contraction mapping, this implies that

(I − M)[S{hF (Z)} − S ∗ {hF (Z)}] ≤ (1 − ) S{hF (Z)} − S ∗ {hF (Z)} . This can only happen when S(hF ) − S ∗ (hF ) = 0. We now complete the proof of Theorem 10.6 by showing that (I − M) is a contraction mapping when M is deﬁned by (10.80). Proof. Theorem 10.6 part (i) Consider the Hilbert space HCZ of all q-dimensional mean-zero measurable functions of (C, Z) equipped with the covariance inner product. The observeddata Hilbert space H and the full-data Hilbert space HF are both contained in HCZ . That is, H ⊂ HCZ , HF ⊂ HCZ are linear subspaces within this space. If we consider any arbitrary element hCZ (C, Z) ∈ HCZ , then ' ( Π[hCZ |H] = E hC,Z (C, Z)|C, GC (Z) (10.85) and

' ( Π[hCZ |HF ] = E hCZ (C, Z)|Z ,

(10.86)

where equations (10.85) and (10.86) can be easily shown to hold by checking that the deﬁnitions of a projection are satisﬁed. Therefore, deriving M{h(·)} = Π[Π[h|H]|HF ] corresponds to ﬁnding two subsequent projections onto these two linear subspaces. What we want to prove is that (I − M) is a contraction mapping from HF to HF . First note that ' ( (I − M)hF (Z) = Π[ hF (Z) − Π[hF (Z)|H] |HF ]. Hence, by the Pythagorean theorem,

(I − M)hF (Z) ≤ hF (Z) − Π[hF (Z)|H] .

(10.87)

Also by the Pythagorean theorem, the right-hand side of (10.87) is equal to '

(1/2

hF (Z) 2 − Π hF (Z)|H 2 . (10.88) ' ( The projection Π[hF (Z)|H] = E hF (Z)|C, GC (Z) , which, by (10.79), equals I(C = r)E{hF (Z)|Gr (Z)}. I(C = ∞)hF (Z) + r=∞

Hence,

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

⎛⎡ ⎜

Π[hF (Z)|H] 2 = E ⎝⎣I(C = ∞)hF (Z) +

⎤T I(C = r)E{hF (Z)|Gr (Z)}⎦

r=∞

⎡ ⎣I(C = ∞)hF (Z) +

261

⎤⎞

I(C = r)E{hF (Z)|Gr (Z)}⎦⎠

r=∞

%

= E I(C = ∞){hF (Z)}T hF (Z) +

I(C = r)

r=∞

T E{hF (Z)|Gr (Z)} × E{hF (Z)|Gr (Z)}

&

≥ E I(C = ∞){hF (Z)}T hF (Z) . Conditioning on Z, (10.89) equals E (∞, Z){hF (Z)}T hF (Z) .

(10.89)

(10.90)

By assumption, (∞, Z) ≥ ∗ > 0 for all Z, and hence (10.90) is ≥ ∗ E[{hF (Z)}T hF (Z)] = ∗ hF (Z) 2 . Consequently, (10.88) is less than or equal to '

hF (Z) 2 − ∗ hF (Z) 2

(1/2

= (1 − ∗ )1/2 hF (Z) , ∗ > 0.

(10.91)

Therefore, by (10.87), (10.88), and (10.91), we have shown that

(I − M)hF (Z) ≤ (1 − ∗ )1/2 hF (Z) for all hF ∈ HF . Hence (I − M) is a contraction mapping.

Obtaining Improved Estimators with Nonmonotone Coarsening In (10.2), we showed that the optimal observed-data inﬂuence function of RAL estimators for β associated with the full-data inﬂuence function ϕF (Z) is obtained by considering the residual after projecting the inverse F (Z) probability weighted complete-case inﬂuence function I(C=∞)ϕ onto Λ2 . (∞,Z) When the coarsening is nonmonotone, we demonstrated in the previous section (see (10.82)) that this residual is equal to L[M−1 {ϕF (Z)}]; that is, L[M−1 {ϕF (Z)}] = J {ϕF (Z)}, where J (ϕF ) was deﬁned by (10.5) and is an element of the space of inﬂuence functions (IF )DR (see Deﬁnition 2). We also argued (see Remark 2) that if we are interested in deriving more eﬃcient estimators (i.e., estimators whose inﬂuence function is an element of (IF )DR ), then we should consider estimating functions, which, at the truth, are elements

262

10 Improving Eﬃciency and Double Robustness with Coarsened Data

of the DR linear space J (ΛF ⊥ ) (see Deﬁnition 3) or, equivalently, the space L{M−1 (ΛF ⊥ )}. Consequently, if we deﬁned a full-data estimating function m(Z, β) such that m(Z, β0 ) ∈ ΛF ⊥ , then we should use L[M−1 {m(Z, β)}] as our observeddata estimating function. That is, the estimator for β would be the solution to the estimating equation n

Li [M−1 i {m(Zi , β)}] = 0.

(10.92)

i=1

Of course, deriving the estimating equation in (10.92) is not trivial. The operator L(·), deﬁned by (10.79), involves ﬁnding conditional expectations of functions of Z given Gr (Z) for r = ∞, and the operator M(·), deﬁned by (10.81), involves ﬁnding such conditional expectations as well as deriving the coarsening probabilities {r, Gr (Z)}. Consequently, in order to proceed, these coarsening probabilities and conditional expectations need to be estimated. The coarsening probabilities are modeled using a parametric model with parameter ψ; that is, P (C = r|Z) = {r, Gr (Z), ψ}, where ψ is estimated by maximizing (8.8) and the resulting estimator is denoted by ψˆn . In order to estimate the conditional expectation of functions of Z given Gr (Z) for r = ∞, we need to estimate the conditional density of Z given Gr (Z). An adaptive strategy is to posit a simplifying parametric model for the density of Z, namely p∗Z (z, ξ), in terms of a parameter ξ, and then estimate ξ by maximizing (10.58), where the estimator is denoted by ξˆn∗ . We remind the reader that the “∗” notation is used to emphasize that such a model is not necessarily believed to contain the true distribution of Z. We assume P → ξ ∗ , where p∗Z (z, ξ ∗ ) may or may suﬃcient regularity conditions so that ξˆn∗ − not be the true density of Z. If it is the true density of Z, we denote this by taking ξ ∗ = ξ0 . With such an estimator for ξ, we can now estimate the conditional expectation E{m(Z, β)|Gr (Z)} by using E{m(Z, β)|Gr (Z), ξˆn∗ }, where E{m(Z, β)|Gr (Z), ξ} is deﬁned by (10.57). The linear operator M and its inverse M−1 are functions of the parameters ψ and ξ, and the linear operator L is a function of ξ. To make this explicit, we deﬁne these operators as M(·, ψ, ξ), M−1 (·, ψ, ξ), and L(·, ξ). Consequently, the improved estimator for β would be the solution to n

ˆ ˆ∗ ˆ∗ Li [M−1 i {m(Zi , β), ψn , ξn }, ξn ] = 0.

(10.93)

i=1

We will now demonstrate that the estimator for β that solves (10.93) is an example of an AIPWCC estimator. This follows because of the following theorem. Theorem 10.7. Let dF (Z, β, ψ, ξ) = M−1 {m(Z, β), ψ, ξ}. Then, L[M−1 {m(Z, β), ψ, ξ}, ξ] = L{dF (Z, β, ψ, ξ), ξ}

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

263

can be written as L{dF (Z, β, ψ, ξ), ξ} =

I(C = ∞)m(Z, β) + L∗2 {C, GC (Z), β, ψ, ξ}, (∞, Z, ψ)

(10.94)

where L∗2 {C, GC (Z), β, ψ, ξ} = ⎡ ⎤ I(C = ∞) ⎣ − {r, Gr (Z), ψ}E{dF (Z, β, ψ, ξ)|Gr (Z), ξ}⎦ (∞, Z, ψ) r=∞ + I(C = r)E{dF (Z, β, ψ, ξ)|Gr (Z), ξ}. (10.95) r=∞

Proof. By deﬁnition, L{dF (Z, β, ψ, ξ), ξ} = I(C = ∞)dF (Z, β, ψ, ξ) +

I(C = r)E{dF (Z, β, ψ, ξ)|Gr (Z), ξ}. (10.96)

r=∞

Because M{dF (Z, β, ψ, ξ), ψ, ξ} = M[M−1 {m(Z, β), ψ, ξ}, ψ, ξ] = m(Z, β) = (∞, Z, ψ)dF (Z, β, ψ, ξ) + {r, Gr (Z), ψ}E{dF (Z, β, ψ, ξ)|Gr (Z), ξ}, r=∞

this implies that −1

F

d (Z, β, ψ, ξ) ={(∞, Z, ψ)} −

m(Z, β)

{r, Gr (Z), ψ}E{dF (Z, β, ψ, ξ)|Gr (Z), ξ} . (10.97)

r=∞

Substituting the right-hand side of (10.97) for dF (Z, β, ψ, ξ) in (10.96) gives us (10.94) and hence proves the theorem. Let us denote L2r {Gr (Z)} to be −E{dF (Z, β, ψ0 , ξ ∗ )|Gr (Z), ξ ∗ }. This is a function of Gr (Z) whether the model p∗Z (z, ξ ∗ ) is correctly speciﬁed or not. Therefore, because of (7.37), where elements of the augmentation space are deﬁned, we note that L∗2 {C, GC (Z), β, ψ0 , ξ ∗ }, deﬁned by (10.95), is an element of the augmentation space Λ2 as long as the model for the coarsening probabilities {r, Gr (Z), ψ0 } is correctly speciﬁed, regardless of whether the posited model for the density of Z, p∗Z (z, ξ ∗ ), is or not. Finally, because of Theorem 10.7, the estimator (10.93) is the same as the solution to n I(Ci = ∞)m(Zi , β) + L∗2 {Ci , GCi (Zi ), β, ψˆn , ξˆn∗ } = 0 (∞, Z, ψˆn ) i=1

(10.98)

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

and therefore is an AIPWCC estimator. The result above assumes that we can derive M−1 {m(Z, β), ψ, ξ}. In Lemma 10.5, we showed that the inverse operator M−1 exists. Nonetheless, this inverse operator is not necessarily easy to compute with nonmonotone coarsened data, and an iterative procedure using successive approximations was also given in Lemma 10.5. Therefore, let us denote, by dF (j) (Z, β, ψ, ξ) the approximation of M−1 {m(Z, β), ψ, ξ} after, say, (j) iterations of successive approximations given by (10.84). Because dF (j) (Z, β, ψ, ξ) is not exactly −1 F ∗ ∗ M {m(Z, β), ψ, ξ}, then L{d(j) (Z, β0 , ψ0 , ξ ), ξ } may not be an element of Λ⊥ and therefore not appropriate as the basis of an estimating function. We therefore suggest the following strategy. Deﬁne L∗2(j) {C, GC (Z), β, ψ, ξ} ⎡ ⎤ I(C = ∞) ⎣ ⎦ =− {r, Gr (Z), ψ}E{dF (j) (Z, β, ψ, ξ)|Gr (Z), ξ} (∞, Z, ψ) r=∞ + I(C = r)E{dF (10.99) (j) (Z, β, ψ, ξ)|Gr (Z), ξ}. r=∞

By construction, L∗2(j) {C, GC (Z), β, ψ0 , ξ ∗ } is an element of Λ2 , whether −1 dF {m(Z, β), ψ, ξ} or not. This implies that (j) (Z, β, ψ, ξ) equals M I(C = ∞)m(Z, β0 ) + L∗2(j) {C, GC (Z), β0 , ψ0 , ξ ∗ } (∞, Z, ψ0 ) is guaranteed to be an element of Λ⊥ when ψ0 is correctly speciﬁed. By deﬁning L∗2(j) (·) in this manner, we are guaranteed that the solution to the equation n I(Ci = ∞)m(Zi , β) + L∗2(j) {Ci , GCi (Zi ), β, ψˆn , ξˆn∗ } = 0 (∞, Z, ψˆn )

(10.100)

i=1

is an AIPWCC estimator. Moreover, because of Theorem 10.7, if we take the number of iterations (j) to be suﬃciently large so that dF (j) (Z, β, ψ, ξ) is −1 equal (or as close as we want) to M {m(Z, β), ψ, ξ}, then solving equation (10.100) will lead to the estimator (10.93). As long as the model for the coarsening probabilities is correctly speciﬁed, the estimator, (10.100), for β, under suitable regularity conditions, will be an RAL estimator for β with inﬂuence function I(C = ∞)ϕF (Z) ∗ + L∗∗ (10.101) {C, G (Z), β , ψ , ξ } − Π [·] Λ C 0 0 ψ , 2(j) (∞, Z, ψ0 ) where

10.5 Improving Eﬃciency when Coarsening Is Nonmonotone

265

−1 ∂m(Z, β0 ) ϕ (Z) = −E m(Z, β0 ), ∂β T F

∗ L∗∗ 2(j) {C, GC (Z), β0 , ψ0 , ξ } −1 ∂m(Z, β0 ) = −E L∗2(j) {C, GC (Z), β0 , ψ0 , ξ ∗ }, ∂β T

and ξ ∗ denotes the limit (in probability) of ξˆn∗ . If, in addition, the posited model p∗Z (z, ξ) contains the truth, then the inﬂuence function (10.101) is equal to L[M−1 {ϕF (Z)}] = J {ϕF (Z)} and hence is an element of (IF )DR . Double Robustness In constructing the estimator in (10.100), we took the point of view that the coarsening probabilities are correctly speciﬁed. We also deﬁned a posited model for Z, namely p∗Z (z, ξ), for the purpose of constructing more eﬃcient estimators. Such a model, for instance, enabled us to construct functions dF (Z) and E{dF (Z)|Gr (Z)}, which were used to derive projections onto the space Λ2 . As we showed above, the model for p∗Z (z, ξ) does not need to be correctly speciﬁed in order for our estimator to be consistent and asymptotically normal as long as the model for the coarsening probabilities is correct. However, as we will now demonstrate, the attempt to gain eﬃciency also gives us the added protection of double robustness. That is, if the posited model for the density of Z is correct (i.e., the true density of Z, p0 (z), is contained in the model p∗Z (z, ξ) for some ξ, which we denote by ξ0 ), and if we choose dF (Z, β, ψ, ξ) to be exactly M−1 {m(Z, β), ψ, ξ}, then the estimator β that is the solution to (10.98) will be consistent and asymptotically normal even if the model for the coarsening probabilities is not correct. Such a double-robustness property was shown previously for two levels of missingness or for monotone coarsening. This result now generalizes the double-robustness property for all coarsened-data models where the coarsening mechanism is CAR. In order for the double-robustness property to hold, we emphasize that dF (Z, β, ψ, ξ) = M−1 {m(Z, β), ψ, ξ}, which, as we showed in the previous section, would result in the estimating equation (10.98) being identical to the estimating equation (10.93). With suﬃcient regularity conditions, the solution to (10.93) will lead to a consistent, asymptotically normal estimator for β if the estimating function of (10.93), L[M−1 {m(Z, β), ψ, ξ}, ξ], evaluated at β = β0 , ψ = ψ ∗ , ξ = ξ ∗ , where ψ ∗ and ξ ∗ are the probabilistic limits of ψˆn and ξˆn∗ respectively, is an unbiased estimator of zero. Namely, we must show that −1 ∗ ∗ ∗ (10.102) E L[M {m(Z, β0 ), ψ , ξ }, ξ ] = 0.

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10 Improving Eﬃciency and Double Robustness with Coarsened Data

Remark 8. The expectation in (10.102) is with respect to the truth. We remind the reader that the true coarsened-data density involves both the marginal density of Z, p0 (z), and the model for the coarsening probabilities, P0 (C = r|Z) = 0 {r, Gr (Z)}. We are considering the case where we posit a model for the marginal density, namely p∗Z (z, ξ), which might be incorrect. We denote this situation by letting the estimator ξˆn∗ converge in probability to ξ ∗ , where p∗Z (z, ξ ∗ ) = p0 (z). However, in the special case where the posited model is correctly speciﬁed, we will denote this by taking ξ ∗ = ξ0 , where p∗Z (z, ξ0 ) = p0 (z). We also posit a model for the coarsening probabilities; namely, P (C = r|Z) = {r, Gr (Z), ψ}. Using the same convention, if this model is misspeciﬁed, we denote this by letting the estimator ψˆn converge in probability to ψ ∗ , where {r, Gr (Z), ψ ∗ } = 0 {r, Gr (Z)}. If this model is correctly speciﬁed, then we take ψ ∗ = ψ0 , where {r, Gr (Z), ψ0 } = 0 {r, Gr (Z)}. To emphasize this notation, we write the expectation in (10.102) as Eξ0 ,ψ0 L[M−1 {m(Z, β0 ), ψ ∗ , ξ ∗ }, ξ ∗ ] .

To demonstrate double robustness, we need to prove the following theorem. Theorem 10.8. (i) Eξ0 ,ψ0 L[M−1 {m(Z, β0 ), ψ0 , ξ ∗ }, ξ ∗ ] = 0, and

(ii) Eξ0 ,ψ0 L[M−1 {m(Z, β0 ), ψ ∗ , ξ0 }, ξ0 ] = 0.

Proof of (i) Because the conditional distribution of C|Z involves the parameter ψ only and the marginal distribution of Z involves ξ only, we can write (i) as Eξ0 Eψ0 L[M−1 {m(Z, β0 ), ψ ∗ , ξ0 }, ξ0 ] Z . (10.103) But, for any function q(Z), Eψ0 [L{q(Z), ξ ∗ }|Z] is, by deﬁnition, equal to M{q(Z), ψ0 , ξ ∗ }. Therefore, (10.103) equals −1 ∗ ∗ Eξ0 M M {m(Z, β0 ), ψ0 , ξ }, ψ0 , ξ = Eξ0 {m(Z, β0 )} = 0. Proof of (ii) Because L{q(Z), ξ0 } = Eξ0 {q(Z)|C, GC (Z)}, then Eξ0 ,ψ0 [L{q(Z), ξ0 }] = Eξ0 {q(Z)}.

10.6 Recap and Review of Notation

267

Notice that the argument above didn’t involve the parameter ψ; hence Eξ0 ,ψ0 [L{q(Z), ξ0 }] = Eξ0 ,ψ∗ [L{q(Z), ξ0 }] = Eξ0 {q(Z)} for any parameter ψ ∗ . Applying this to the left-hand side of equation (ii), we obtain −1 ∗ Eξ0 ,ψ0 L[M {m(Z, β0 ), ψ , ξ0 }, ξ0 ] −1 ∗ = Eξ0 ,ψ∗ L[M {m(Z, β0 ), ψ , ξ0 }, ξ0 ] −1 ∗ = Eξ0 Eψ∗ L[M {m(Z, β0 ), ψ , ξ0 }, ξ0 ] Z −1 ∗ ∗ = Eξ0 M M {m(Z, β0 ), ψ , ξ0 }, ψ , ξ0 = Eξ0 {m(Z, β0 )} = 0. Remark 9. As we indicated earlier, in order for our estimator to be double robust, we must make sure that if the posited model p∗Z (z, ξ) contains the truth, then the estimator for ξ, namely ξˆn∗ , is a consistent estimator for ξ0 , even if the model for the coarsening probabilities is misspeciﬁed. Therefore, likelihood estimators such as those that maximize (10.58) would be appropriate for this purpose, whereas AIPWCC estimators for ξ would not.

10.6 Recap and Review of Notation General results •

Among observed-data inﬂuence functions ϕ{C, GC (Z)} ∈ (IF ), I(C = ∞)ϕF (Z) ϕ{C, GC (Z)} = + L2 {C, GC (Z)} − Π{[ ]|Λψ }, (∞, Z, ψ0 )

where ϕ (Z) is a full-data inﬂuence function and L2 {C, GC (Z)} ∈ Λ2 , F

optimal inﬂuence functions can be obtained by taking I(C = ∞)ϕF (Z) Λ2 . L2 {C, GC (Z)} = −Π (∞, Z, ψ0 ) •

We denote such inﬂuence functions using the linear operator J (·), where J : HF → H is deﬁned as I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) F Λ2 . J (ϕ ) = −Π (∞, Z, ψ0 ) (∞, Z, ψ0 )

268

•

10 Improving Eﬃciency and Double Robustness with Coarsened Data

We denote the class of inﬂuence functions J {(IF )F } = J (ϕF ) : ϕF (Z) ∈ (IF )F

by (IF )DR (i.e., the space of double-robust inﬂuence functions). • The corresponding linear space used to derive estimating functions that lead to observed-data estimators for β with inﬂuence functions in (IF )DR is denoted by the DR linear space and is deﬁned as J (ΛF ⊥ ). Two levels of missingness •

Let the full data be given by Z = (Z1T , Z2T )T , where Z1 is always observed but Z2 may be missing. Let R denote the complete-case indicator, and P (R = 1|Z) = π(Z1 , ψ) is a model that describes the complete-case probabilities. Then, for ϕ∗F (Z) ∈ ΛF ⊥ , R − π(Z1 , ψ0 ) Rϕ∗F (Z) − E{ϕ∗F (Z)|Z1 } . J (ϕ∗F ) = π(Z1 , ψ0 ) π(Z1 , ψ0 )

• Adaptive double-robust AIPWCC estimators for β are obtained by solving the equation " n Ri m(Zi , β) Ri − π(Z1i , ψˆn ) − h∗2 (Z1i , β, ξˆn∗ ) = 0, π(Z1i , ψˆn ) π(Z1i , ψˆn ) i=1

where m(Z, β) is a full-data estimating function, ψˆn is the MLE for ψ obtained by maximizing n !

{π(Z1i , ψ)}Ri {1 − π(Z1i , ψ)}1−Ri ,

i=1

ξˆn∗ is an estimator for the parameter ξ in a posited model p∗Z (z, ξ), and h∗2 (Z1i , β, ξ) = E m(Zi , β)|Z1i , ξ . Monotone coarsening • When data are monotonically coarsened, the coarsening probabilities can be modeled, as a function of the parameter ψ, using the discrete hazard probability of coarsening, λr {Gr (Z), ψ} = P (C = r|C ≥ r, Z, ψ), r = ∞.

10.6 Recap and Review of Notation

•

269

For ϕ∗F (Z) ∈ ΛF ⊥ , I(C = ∞)ϕ∗F (Z) J (ϕ∗F ) = (∞, Z, ψ0 ) I(C = r) − λr {Gr (Z), ψ0 }I(C ≥ r) ' ( E ϕ∗F (Z)|Gr (Z) , + Kr {Gr (Z), ψ0 } r=∞

where Kr {Gr (Z), ψ0 } = P (C > r|Z, ψ0 ) =

r !

[1 − λr {Gr (Z), ψ0 }], r = ∞,

r =1

and (∞, Z, ψ0 ) =

!

[1 − λr {Gr (Z), ψ0 }].

r=∞

• Adaptive double-robust AIPWCC estimators for β are obtained by solving the equation % n I(Ci = ∞)m(Zi , β) + (∞, Zi , ψˆn ) i=1 # & $ I(Ci = r) − λr {Gr (Zi ), ψˆn }I(Ci ≥ r) ∗ ˆ E{m(Z, β)|Gr (Zi ), ξn } = 0, Kr {Gr (Zi ), ψˆn } r=∞

where m(Z, β) is a full-data estimating function, ψˆn is the MLE for ψ, and ξˆn∗ is an estimator for the parameter ξ in a posited model p∗Z (z, ξ). Nonmonotone coarsening • In general, for ϕ∗F (Z) ∈ ΛF ⊥ , J (ϕ∗F ) = L{M−1 (ϕ∗F )}, where L(hF ) = E{hF (Z)|C, GC (Z)} is equal to I(C = r)E{hF (Z)|Gr (Z)}, r

M(hF ) = E{L(hF )|Z} is equal to {r, Gr (Z)}E{hF (Z)|Gr (Z)}, r

270

10 Improving Eﬃciency and Double Robustness with Coarsened Data

and where the inverse operator M−1 (hF ) exists and can be obtained by successive approximation, where ϕn+1 (Z) = (I − M)ϕn (Z) + hF (Z), n→∞

•

and ϕn (Z) −−−−→ M−1 {hF (Z)}. More eﬃcient adaptive AIPWCC estimators for β can be obtained by solving the equation n I(Ci = ∞)m(Zi , β) + L∗2(j) {Ci , GCi (Zi ), β, ψˆn , ξˆn∗ } = 0, (∞, Z, ψˆn ) i=1

where m(Z, β) is a full-data estimating function, ψˆn is the MLE for ψ, ξˆn∗ is an estimator for the parameter ξ in a posited model p∗Z (z, ξ), L∗2(j) {C, GC (Z), β, ψ, ξ} ⎡ ⎤ I(C = ∞) ⎣ ⎦ =− {r, Gr (Z), ψ}E{dF (j) (Z, β, ψ, ξ)|Gr (Z), ξ} (∞, Z, ψ) r=∞ + I(C = r)E{dF (j) (Z, β, ψ, ξ)|Gr (Z), ξ}, r=∞ −1 and dF {m(Z, β, ψ, ξ)} after (j) (j) (Z, β, ψ, ξ) is the approximation to M iterations of successive approximations. • As j → ∞, the AIPWCC estimator becomes a double-robust estimator.

10.7 Exercises for Chapter 10 1. In Deﬁnition 6 of Chapter 3, we deﬁned a q-replicating linear space. In Theorem 10.1, we considered the linear space M = Π[Λ2 |Λ⊥ ψ ] ⊂ H. a) Prove that Λ2 is a q-replicating linear space. b) Prove that Λψ is a q-replicating linear space. (Recall that Λψ is the ﬁnite-dimensional linear space, contained in H, that is spanned by the score vector Sψ {C, GC (Z)}.) c) Prove that M is a q-replicating linear space. 2. When we considered monotone coarsening, we stated that the adaptive estimator βˆn , which solves equation (10.60), is asymptotically equivalent to the AIPWCC estimator βˆn∗ , which solves equation (10.61), when the model for the coarsening probabilities is correctly speciﬁed. Give a heuristic proof that P → 0. n1/2 (βˆn − βˆn∗ ) − (You can use arguments similar to the proof of Theorem 10.3.)

10.7 Exercises for Chapter 10

271

3. Consider the simple linear regression restricted moment model where with full data (Yi , X1i , X2i ), i = 1, . . . , n, we assume E(Yi |X1i , X2i ) = β0 + β1 X1i + β2 X2i . In such a model, we can estimate the parameters (β0 , β1 , β2 )T using ordinary least squares; that is, the solution to the estimating equation n

(1, X1i , X2i )T (Yi − β0 − β1 X1i − β2 X2i ) = 0.

(10.104)

i=1

In fact, this estimator is locally eﬃcient when var(Yi |X1i , X2i ) is constant. The data, however, are missing at random with a monotone missing pattern. That is, Yi is observed on all individuals in the sample; however, for some individuals, only X2i is missing, and for others both X1i and X2i are missing. Therefore, we deﬁne the missingness indicator (Ci = 1) if we only observe Yi , (Ci = 2) if we only observe (Yi , X1i ), and (Ci = ∞) if we observe (Yi , X1i , X2i ). We will deﬁne the missingness probability model using discrete-time hazards, namely λ1 (Y ) = P (C = 1|Y ), λ2 (Y, X1 ) = P (C = 2|C ≥ 2, Y, X1 ). a) In terms of λ1 and λ2 , what is P (C = ∞|Y, X1 , X2 )? In order to model the missingness process, we assume logistic regression models; namely, p , logit {λ1 (Y )} = ψ10 + ψ11 Y, where logit (p) = log 1−p and logit {λ2 (Y, X1 )} = ψ20 + ψ21 X1 + ψ22 Y. b) Using some consistent notation to describe the observed data, write out the estimating equations that need to be solved to derive the maximum likelihood estimator for ψ = (ψ10 , ψ11 , ψ20 , ψ21 , ψ22 )T .

272

10 Improving Eﬃciency and Double Robustness with Coarsened Data

c) Describe the linear subspace Λψ . d) Describe the linear subspace Λ2 . Verify that Λψ ⊂ Λ2 . e) Describe the subspace Λ⊥ , the linear space orthogonal to the observeddata nuisance tangent space. An initial estimator for β can be obtained by using an inverse probability weighted complete-case estimator that solves the equation n

I(Ci = ∞)

i=1

(∞, Yi , X1i , X2i , ψˆn )

(1, X1i , X2i )T (Yi −β0 −β1 X1i −β2 X2i ) = 0,

where ψˆn is the maximum likelihood estimator derived in (b). Denote this estimator by βˆnI . f) What is the i-th inﬂuence function for βˆnI ? g) Derive a consistent estimator for the asymptotic variance of βˆnI . In an attempt to gain eﬃciency, we consider I(Ci = ∞) ϕ∗F (Yi , X1i , X2i ) (∞, Yi , X1i , X2i , ψo ) I(Ci = ∞)ϕ∗F (Y1i , X1i , X2i ) Λ2 , −Π (∞, Yi , X1i , X2i , ψo ) where ϕ∗F (·) = (1, X1i , X2i )T (Yi − β0 − β1 X1i − β2 X2i ). h) Compute I(Ci = ∞)ϕ∗F (Y1i , X1i , X2i ) Λ2 . Π (∞, Yi , X1i , X2i , ψo ) In practice, we need to estimate (h) using a simplifying model. For simplicity, let us use as a working model that (Yi , X1i , X2i )T is multivariate normal with mean (µY , µX1 , µX2 )T and covariance matrix ⎡ ⎤ σY Y σY X1 σY X2 ⎣σY X1 σX1 X2 σX1 X2 ⎦ . σY X2 σX1 X2 σX2 X2 i) With the observed data, how would you estimate the parameters in the multivariate normal? j) Assuming the simplifying multivariate normal model and the estimates derived in (i), estimate the projection in (h). k) Write out the estimating equation that needs to be solved to get an improved estimator. l) Find a consistent estimator for the asymptotic variance of the estimator in (k). (Keep in mind that the simplifying model of multivariate normality may not be correct.)

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

Using semiparametric theory, we have demonstrated that RAL estimators for the parameter β in a semiparametric model with coarsened data can be obtained using AIPWCC estimators. That is, estimators for β can be obtained from a sample of coarsened data {Ci , GCi (Zi )}, i = 1, . . . , n, by solving the estimating equation # $ n I(Ci = ∞)m(Zi , β) (11.1) + L2 {Ci , GCi (Zi ), β, ψˆn } = 0, (∞, Zi , ψˆn ) i=1 where m(Z, β) is a full-data estimating function, L2 (·) is an element of the augmentation space, Λ2 , and ψˆn is an estimator for the parameters in the coarsening model. In Chapter 10, we demonstrated that, among the AIPWCC estimators, improved double-robust estimators for β can be obtained by considering observed-data estimating functions within the class J (ΛF ⊥ ) (i.e., the so-called DR linear space), where, for ϕ∗F ∈ ΛF ⊥ , I(C = ∞)ϕ∗F I(C = ∞)ϕ∗F −Π |Λ2 . J (ϕ∗F ) = (∞, Z) (∞, Z) This led us to develop adaptive estimators that were the solution to # $ n I(Ci = ∞)m(Zi , β) ∗ ∗ + L2 {Ci , GCi (Zi ), β, ψˆn , ξˆn } = 0, (∞, Zi , ψˆn )

(11.2)

i=1

where L∗2 {C, GC (Z), β, ψ, ξ} is equal to minus the projection onto the augmentation space; i.e., I(C = ∞)m(Z, β) |Λ2 . −Π (∞, Z) To compute this projection, we need estimates for the parameter ψ that describes the coarsening probabilities and an estimate for the marginal distribution of Z. The latter is accomplished by positing a simpler, and possibly

274

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

incorrect, model for the density of Z as p∗Z (z, ξ) and deriving an estimator ξˆn∗ for ξ. Among the class of double-robust estimators is the eﬃcient estimator, the estimator that achieves the semiparametric eﬃciency bound. Finding this eﬃcient estimator within this class of double-robust estimators entails deriving the proper choice of the full-data estimating function m(Z, β), where m(Z, β0 ) = ϕ∗F ∈ ΛF ⊥ . In this chapter, we will study how to ﬁnd the eﬃcient estimator and the appropriate choice for m(Z, β). As we will see, the eﬃcient estimator will depend on the true marginal distribution of Z, which, of course, is unknown to us. Consequently, we will develop adaptive methods where the eﬃcient estimator will be computed based on a posited model p∗Z (z, ξ) for the density of Z. Hence, the proposed methods will lead to a locally eﬃcient estimator, an estimator for β that will achieve the semiparametric eﬃciency bound if the posited model is correct but will still be a consistent, asymptotically normal RAL semiparametric estimator for β even if the posited model does not contain the truth. As we indicated in Chapter 10, ﬁnding improved double-robust estimators often involves computationally intensive methods. In fact, when the coarsening of the data is nonmonotone, these computational challenges could be overwhelming. Similarly here, deriving locally eﬃcient estimators involves numerical diﬃculties. Nonetheless, the theory developed by Robins, Rotnitzky, and Zhao (1994) gives us a prescription for how to derive locally eﬃcient estimators. We present this theory in this chapter and discuss strategies for ﬁnding locally eﬃcient estimators. The methods build on the full-data semiparametric theory. Therefore, it will be assumed that we have a good understanding of the full-data semiparametric model. That is, we can identify the space orthogonal to the full-data nuisance tangent space ΛF ⊥ , the class F of full-data inﬂuence functions (IF )F , the full-data eﬃcient score Seﬀ (Z, β0 ), F and the full-data inﬂuence function ϕeﬀ (Z). However, we caution the reader that these methods may be very diﬃcult to implement in practice, and we believe a great deal of research still needs to be done in developing feasible computational algorithms. In Chapter 12, we will discuss approximations that may be used to derive AIPWCC estimators for β that although not locally eﬃcient are easier to implement and can result in substantial gains in eﬃciency. There is, however, one class of problems where locally eﬃcient estimators are obtained readily, and this is the case when only one full-data inﬂuence function exists. This occurs, for example, when the full-data tangent space is the entire Hilbert space HF , as is the case when no restrictions are put on the class of densities for Z; i.e., the nonparametric problem (see Theorem 4.4). In Section 5.3, we showed that only one full-data inﬂuence function exists when we are interested in estimating the mean of a random variable in a nonparametric problem and that this estimator can be obtained using the sample average. When only one full-data inﬂuence function, ϕF (Z), exists, then the class of observed-data inﬂuence functions is given by

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

275

I(C = ∞)ϕF (Z) + L2 {C, GC (Z)} − Π{[ ]|Λψ }, L2 (·) ∈ Λ2 , (∞, Z, ψ0 )

and because of Theorem 10.1, the optimal observed-data inﬂuence function is I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) Λ2 , −Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) which is also the eﬃcient inﬂuence function. Consequently, when there is only one full-data inﬂuence function, then the adaptive double-robust estimator outlined in Chapter 10 will lead to a locally eﬃcient estimator. We illustrate with a simple example. Example: Estimating the Mean with Missing Data In Section 7.4, we considered a problem where interest focused on estimating the relationship of a response variable Y as a function of covariates X. Because one of the covariates X2 was expensive to collect, only a subsample of this covariate was collected with probability π(Y, X1 ), which depends on the response Y and the other covariates X1 . Thus, for this problem, X = (X1T , X2 )T and the full data Z = (Y, X). The probability π(Y, X1 ) was chosen by the investigator and therefore this is an example of two levels of missingness by design. The complete-case indicator is denoted by R, where P (R = 1|Z) = P (R = 1|Y, X1 ) = π(Y, X1 ), and the observed data are denoted by O = (R, Y, X1 , RX2 ). The statistical question, as originally stated in Section 7.4, was to estimate the parameter in a restricted moment model with a sample of observed data (Ri , Yi , X1i , Ri X2i ), i = 1, . . . , n. However, we now want to consider the simpler problem of estimating the mean of X2 using the observed data. Let us denote this parameter as β = E(X2 ). Also, to be as robust to model misspeciﬁcation as possible, we no longer assume any speciﬁc relationship between the response Y and the covariates X. Consequently, with no assumptions on the joint distribution of the full data (Y, X1 , X2 ) (i.e., the nonparametric problem), we know that there is only one full-data inﬂuence function of RAL estimators for β = E(X2 ), namely ϕF (Z) = (X2 − β0 ), and that the solution to the full-data estimating equation is n (X2i − β) = 0; i=1

n i.e., the sample mean βˆnF = n−1 i=1 X2i is an RAL full-data estimator for β with this inﬂuence function. We also know that all observed-data inﬂuence functions for this problem are given by RϕF (Z) R − π(Y, X1 ) (11.3) + L(Y, X1 ) , π(Y, X1 ) π(Y, X1 )

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11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

where L(Y, X1 ) is an arbitrary function of Y and X1 , and, because of Theorem 10.2, the optimal choice for L(Y, X1 ) is given by −E(X2 |Y, X1 ). Therefore, among the class of inﬂuence functions (11.3), the one with the smallest variance is RϕF (Z) R − π(Y, X1 ) (11.4) − E(X2 |Y, X1 ) . π(Y, X1 ) π(Y, X1 ) This also is the semiparametric eﬃcient observed-data inﬂuence function. Since the eﬃcient inﬂuence function depends on E(X2 |Y, X1 ), we consider an adaptive strategy. Using methods described in Section 10.2 for adaptive estimation with two levels of missingness, we posit a model for the conditional distribution of X2 given (Y, X1 ). One simple model we may consider is that the distribution of X2 given (Y, X1 ) is normally distributed with mean ξ1 + ξ2 Y +ξ3T X1 and variance σξ2 . This is attractive because the MLE estimator for the parameter ξ (i.e., the estimator that maximizes (10.16)) can be obtained using ordinary least squares among the complete cases {i : Ri = 1}. That is, the estimator for ξ is obtained as the solution to the estimating equation n

T T Ri (1, Yi , X1i ) (X2i − ξ1 − ξ2 Yi − ξ3T X1i ) = 0.

i=1

Denote the least-squares estimator for ξ by ξˆn∗ . Then, the adaptive observeddata estimator for β is given as the solution to n i=1

Ri Ri − π(Y1 , X1i ) ˆ∗ ∗ ∗T ˆ ˆ (X2i − β) − (ξ1n + ξ2n Yi + ξ2n X1i − β) π(Y1 , X1i ) π(Y1 , X1i )

= 0, which, after solving, yields n

Ri − π(Y1 , X1i ) ˆ∗ Ri X2i ∗ ∗T ˆ ˆ − (ξ1n + ξ2n Yi + ξ2n X1i ) . π(Y1 , X1i ) π(Y1 , X1i ) i=1 (11.5) This estimator is a consistent, asymptotically normal observed-data RAL estimator for β regardless of whether the posited model is correct or not. Moreover, if the posited model is correctly speciﬁed, then this estimator is semiparametric eﬃcient. Therefore, (11.5) is a locally eﬃcient semiparametric estimator for β = E(X2 ). We also note that because the least-squares estimator leads to a consistent estimator for ξ, if the conditional expectation of X2 given Y and X1 is linear, namely βˆn = n−1

E(X2 |Y, X1 ) = ξ1 + ξ2 Y + ξ3T X1 ,

(11.6)

whether the distribution is normal or not, the locally eﬃcient estimator βˆn is also fully eﬃcient whenever (11.6) is satisﬁed.

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277

11.1 The Observed-Data Eﬃcient Score As we know, the eﬃcient RAL estimator for β will have an inﬂuence function that is proportional to the eﬃcient score. Therefore, it is useful to study the properties of the eﬃcient score with coarsened data. Toward that end, we give two diﬀerent representations for the eﬃcient score. The ﬁrst is likelihood-based and the second is based on AIPWCC estimators. The relationship of these two representations to each other will be key in the development of the proposed adaptive locally eﬃcient estimators. Representation 1 (Likelihood-Based) We remind the reader that the eﬃcient observed-data estimator for β has an inﬂuence function that is proportional to the observed-data eﬃcient score and that the eﬃcient score is unique and equal to Seﬀ {C, GC (Z)} = Sβ {C, GC (Z)} − Π[Sβ {C, GC (Z)}|Λ], where Sβ {C, GC (Z)} = E{SβF (Z)|C, GC (Z)}, and Λ = Λψ ⊕ Λη , Λψ ⊥ Λη . Because Λψ ⊥ Λη , this implies that Π[Sβ {C, GC (Z)}|Λ] = Π[Sβ {C, GC (Z)}|Λψ ] + Π[Sβ {C, GC (Z)}|Λη ]. The same argument that was used to show that Λη ⊥ Λ2 in Theorem 8.2 can also be used to show that Sβ {C, GC (Z)} ⊥ Λ2 . Since Λψ ⊂ Λ2 , this implies that Sβ {C, GC (Z)} is orthogonal to Λψ . Therefore, Π[Sβ {C, GC (Z)}|Λψ ] = 0,

(11.7)

which implies that Π[Sβ {C, GC (Z)}|Λ] = Π[Sβ {C, GC (Z)}|Λη ], and the eﬃcient score is Seﬀ {C, GC (Z)} = Sβ {C, GC (Z)} − Π[Sβ {C, GC (Z)}|Λη ].

(11.8)

Recall that ( ' ' ( Λη = E αF (Z)|C, GC (Z) for all αF (Z) ∈ ΛF . This means that the unique projection Π[Sβ {C, GC (Z)}|Λη ] corresponds to some element in Λη , which we will denote by ' F ( F E αeﬀ (Z)|C, GC (Z) , αeﬀ (Z) ∈ ΛF . With this representation, ' ( ' F ( Seﬀ {C, GC (Z)} = E SβF (Z)|C, GC (Z) − E αeﬀ (Z)|C, GC (Z) F (Z)}|C, GC (Z)]. = E[{SβF (Z) − αeﬀ

(11.9)

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11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

Remark 1. The full-data eﬃcient score is given by F Seﬀ (Z) = SβF (Z) − Π[SβF (Z)|ΛF ]. F (Z) is not necessarily the same as Π[SβF (Z)|ΛF ]. However, the element αeﬀ This means that, in general, ' F ( Seﬀ {C, GC (Z)} = E Seﬀ (Z)|C, GC (Z) . (11.10)

Representation 2 (AIPWCC-Based) In (10.2), we derived the optimal (smallest variance matrix) inﬂuence function among the class of AIPWCC inﬂuence functions associated with a ﬁxed fulldata inﬂuence function ϕF (Z). Consequently, we can restrict our search for the eﬃcient observed-data inﬂuence function to the class of inﬂuence functions I(C = ∞)ϕF (Z) I(C = ∞)ϕF (Z) F F Λ2 : ϕ (Z) ∈ (IF ) , −Π (∞, Z, ψ0 ) (∞, Z, ψ0 ) which we denote as the space (IF )DR = J {(IF )F }. Since the observed-data eﬃcient score is deﬁned up to a proportionality constant matrix times the eﬃcient inﬂuence function, this implies that the observed-data eﬃcient score must be an element in the DR linear space J (ΛF ⊥ ), I(C = ∞)B F (Z) I(C = ∞)B F (Z) −Π Λ2 : for B F (Z) ∈ ΛF ⊥ , (∞, Z, ψ0 ) (∞, Z, ψ0 ) (11.11) F with the corresponding element denoted by Beﬀ (Z). Relationship between the Two Representations Thus, we have shown that there are two equivalent representations for the observed-data eﬃcient score, which are given by (11.9) and (11.11): F F F (i) Seﬀ {C, GC (Z)} = E Sβ (Z) − αeﬀ (Z) C, GC (Z) , where αeﬀ (Z) ∈ ΛF , and (ii) Seﬀ {C, GC (Z)} =

F F (Z) (Z) I(C = ∞)Beﬀ I(C = ∞)Beﬀ Λ2 , where −Π (∞, Z) (∞, Z)

F Beﬀ (Z) ∈ ΛF ⊥ .

Remark 2. If the full-data model were parametric (i.e., if η were ﬁnitedimensional), then representation (i) would correspond to the estimating function that would be used to derive the coarsened-data MLE for β. This may

11.1 The Observed-Data Eﬃcient Score

279

be preferable, as it does not involve the parameter ψ specifying the coarsening process. However, if the model is complicated, this approach may become formidable due to the curse of dimensionality. The second representation leads us to augmented inverse-probability weighted complete-case (AIPWCC) estimating equations. These estimators, which build on full-data estimators, may be easier to derive, even in some complicated situations. However, this approach requires that the data analyst model the coarsening process and estimate the parameter ψ. Also, to obtain gains in eﬃciency, an adaptive approach is required, where the data analyst posits simpler models for the full data p∗Z (z, ξ), in terms of the parameter ξ that needs to be estimated. Which method is preferable often depends on the speciﬁc application. However, because of the robustness of the AIPWCC estimators to misspeciﬁcation, we will focus attention on these estimators. Nonetheless, the two representations will aid us in getting a better understanding of the geometry of observed-data eﬃcient inﬂuence functions and guide us in ﬁnding as good an AIPWCC estimator as is feasible. F If we knew, or could reasonably approximate, the element Beﬀ (Z) ∈ ΛF ⊥ of representation (ii) above, then we could construct an AIPWCC estimator for β by using the full-data estimating function m(Z, β), where m(Z, β0 ) = F Beﬀ (Z), and applying the methods outlined in Chapter 10. Subject to the accuracy of diﬀerent posited models, this methodology will give us as eﬃcient an AIPWCC estimator as possible while still aﬀording us maximum robustness F to misspeciﬁcation. Toward that end, we now show how to derive Beﬀ (Z) in the following theorem. F Theorem 11.1. The element Beﬀ (Z) is the unique B F (Z) ∈ ΛF ⊥ that solves the equation F Π[M−1 {B F (Z)}|ΛF ⊥ ] = Seﬀ (Z), (11.12)

where M(·) denotes the linear operator, given by Deﬁnition 5 of Chapter 10, equation (10.80), which maps HF (full-data Hilbert space) to HF as ' ( M{hF (q×1) (Z)} = E E hF (Z) C, GC (Z) Z . Proof. Because of the equivalence of the two representations (i) and (ii) above, the eﬃcient score can be written as ' ( F E SβF (F ) − αeﬀ (Z) C, GC (Z) F F (Z) (Z) I(C = ∞)Beﬀ I(C = ∞)Beﬀ Λ2 (11.13) = −Π (∞, Z) (∞, Z) F F for some αeﬀ (Z) ∈ ΛF and Beﬀ (Z) ∈ ΛF ⊥ . Taking the conditional expectation of both sides of equation (11.13) with respect to Z, we obtain the equation ' ( F F M SβF (Z) − αeﬀ (Z) = Beﬀ (Z). (11.14)

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11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

In Theorem 10.6 and Lemma 10.5, we showed that the linear operator M(·) has a unique inverse, M−1 . Therefore, we can write (11.14) as ' F ( ' ( F M−1 Beﬀ (Z) = SβF (Z) − αeﬀ (Z) . (11.15) Projecting both sides of equation (11.15) onto ΛF ⊥ , we obtain ' F F ( Π M−1 Beﬀ (Z) ΛF ⊥ = Π SβF (Z) ΛF ⊥ − Π αeﬀ (Z) ΛF ⊥ . * +, || || F Seﬀ (Z)

0 F (Z) since αeﬀ ∈ ΛF

This leads us to the important relationship that the eﬃcient element F (Z) ∈ ΛF ⊥ , which is used to construct the eﬃcient score Beﬀ F F (Z) (Z) I(C = ∞)Beﬀ I(C = ∞)Beﬀ Λ2 , (11.16) Seﬀ {C, GC (Z)} = −Π (∞, Z) (∞, Z) must satisfy the relationship (11.12). F We still need to show that a unique Beﬀ (Z) ∈ ΛF ⊥ exists that satisﬁes F (11.12) and ﬁnd a method for computing Beﬀ (Z). F (Z) Uniqueness of Beﬀ F (Z) ∈ ΛF ⊥ that solves the equation Lemma 11.1. There exists a unique Beﬀ F F Π M−1 {Beﬀ (Z)}|ΛF ⊥ = Seﬀ (Z), (11.17)

and this solution can be obtained through successive approximations. Proof. Notice that (11.17) involves a mapping from the linear subspace ΛF ⊥ ⊂ HF to ΛF ⊥ ⊂ HF . The way we will prove this lemma is by deﬁning another linear mapping (I −Q){M−1 }(·) : HF → HF , which maps the entire full-data Hilbert space to the entire full-data Hilbert space in such a way that (i) (I − Q){M−1 }(·) coincides with the mapping Π[M−1 (hF )|ΛF ⊥ ] whenever hF ∈ ΛF ⊥ , and (ii) (I − Q) is a contraction mapping and hence has a unique inverse. Deﬁne

F F Deﬀ (Z) = M−1 {Beﬀ (Z)}.

F Because of the existence and uniqueness of M−1 , if Beﬀ (Z) exists, then so F does Deﬀ (Z) such that F (Z)} ∈ ΛF ⊥ . (11.18) M{Deﬀ F Motivated by the fact that Deﬀ (Z) must satisfy

11.1 The Observed-Data Eﬃcient Score

281

(a) equation (11.17), namely F F (Z)|ΛF ⊥ ] = Seﬀ (Z), Π[Deﬀ

(11.19)

F F {I(·) − Π[I(·)|ΛF ]}{Deﬀ (Z)} = Seﬀ (Z),

(11.20)

or, equivalently,

where we view {I(·) − Π[I(·)|ΛF ]}(·) as a linear operator from HF to HF with I(·) denoting the identity operator, and (b) equation (11.18), or, equivalently, F (Z)} = 0, Π[M(·)|ΛF ]{Deﬀ

(11.21)

where Π[M(·)|ΛF ](·) is also viewed as a linear operator from HF to HF , we combine (11.19)–(11.21) to consider the equations Seﬀ (Z) = Π[hF (Z)|ΛF ⊥ ] + Π[M{hF (Z)}|ΛF ] = (I − Q){h (Z)}, F

(11.22) (11.23)

where (I − Q)(·) is a linear operator, with Q(·) deﬁned as F F (Z)} = Π[(I − M){Deﬀ (Z)} ΛF ]. Q{Deﬀ

We ﬁrst argue that the solution, hF (Z) ∈ HF , to equation (11.23) exists and F is unique and then argue that this solution hF (Z) must equal Deﬀ (Z). According to Lemma 10.5, the linear operator (I −Q)(·) will have a unique inverse if we can show that the linear operator “Q” is a contraction mapping. Also, if Q is a contraction mapping, then by Lemma 10.5, the unique inverse is equal to ∞ (I − Q)−1 = Qi . i=0

In the proof of Theorem 10.6, part (i), we already showed that (I − M) is a contraction mapping; i.e., ||(I − M){hF }|| ≤ (1 − ε)||hF ||.

(11.24)

By the Pythagorean theorem, ||Q(hF )|| = ||Π[(I − M)hF ΛF ]|| ≤ ||(I − M)hF || ≤ (1 − ε)||hF || by (11.24). Hence, Q is a contraction mapping and (I − Q)−1 exists and is unique.

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11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

To complete the proof, we must show that the unique solution hF (Z) to F equation (11.23) or, equivalently, (11.22), is identical to Deﬀ (Z) satisfying (11.19) and (11.21). F Clearly, any element Deﬀ (Z) satisfying (11.19) and (11.21) must satisfy (11.22). Conversely, since Seﬀ (Z) ∈ ΛF ⊥ , then the solution hF (Z) of (11.22) must be such that Π[M{hF (Z)}|ΛF ] = 0 and Π[hF (Z)|ΛF ⊥ ] = Seﬀ (Z); that is, hF (Z) satisﬁes (11.21) and (11.19), respectively. This completes the proof F that Deﬀ (Z) exists and is the unique element satisfying equations (11.19) and F (11.21) or, equivalently, that Beﬀ (Z) = M{Deﬀ (Z)} exists and is the unique solution to (11.17). F (Z) can be obtained by In Lemma 10.5, we showed that the solution Deﬀ successive approximation; that is, F D(i+1) (Z) = Π (I − M)D(i) (Z) ΛF + Seﬀ (Z), (11.25) and

F D(i) (Z) −−−→ Deﬀ (Z). i→∞

If we deﬁne

B (i) (Z) = Π M{D(i) (Z)} ΛF ⊥ ,

where, by construction, B (i) (Z) ∈ ΛF ⊥ , then i→∞ F (Z). B (i) (Z) = M{D(i) (Z)} −Π M{D(i) (Z)} ΛF −−−→ Beﬀ * +, ⇓ ⇓ F F F M{Deﬀ (Z)} = Beﬀ (Z) Π Beﬀ (Z)|ΛF || 0

(11.26)

The inverse operator M−1 plays an important role in the deﬁnition of given by Theorem 11.1. As we showed in Chapter 10, M−1 exists, is unique, and can be computed using successive approximations. However, a closed-form solution exists when the coarsening is monotone. For completeness, we give this result.

F Beﬀ (Z)

M−1 for Monotone Coarsening Recall that, for monotone coarsening, we deﬁned coarsening probabilities using discrete hazard probabilities λr {Gr (Z)} = P (C = r|C ≥ r, Z) and Kr {Gr (Z)} = P (C ≥ r + 1|Z) =

r ! j=1

[1 − λj {Gj (Z)}].

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283

Theorem 11.2. When coarsening is monotone, the inverse operator M−1 is given by aF (Z) = M−1 {hF (Z)} =

λr hF (Z) − E{hF (Z)|Gr (Z)}, (∞, Z) Kr

(11.27)

r=∞

where we use the shorthand notation λr = λr {Gr (Z)} and Kr = Kr {Gr (Z)}. An equivalent representation is also given by M−1 {hF (Z)} = hF (Z) +

λr hF (Z) − E{hF (Z)|Gr (Z)} . Kr

(11.28)

r=∞

Proof. In Theorem 10.6, we showed that M−1 exists and is uniquely deﬁned. Therefore, we only need to show that M{aF (Z)} = hF (Z), where aF (Z) is deﬁned by (11.27) and M(aF ) = ∞ aF + r E(aF |Gr ). r=∞

We note that

λr = Kr

1 1 − Kr Kr−1

(11.29)

and ∞ = K , where denotes the number of coarsening /levels; i.e., 0 denotes the largest λr 1 for K and K1 for value of C less than ∞. After substituting K1r − Kr−1 r ∞ in (11.27) and rearranging terms, we obtain 1 F F F E(h |Gr ) − E(h |Gr−1 ) , a = (11.30) Kr−1 r where K0 = 1 and E(hF |G0 ) = 0. Therefore, 1 F F F M(a ) = E(h |Gr ) − E(h |Gr−1 ) Gr r E Kr−1 r r F F h h = E Gr . Gr − E Gr−1 r E Kr−1 Kr−1 r r

(11.31) As a consequence of monotone coarsening and the laws of conditional expectations, we obtain that

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11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

E

F F h h Gr Gr − E Gr−1 E Kr−1 Kr−1 = =

1 Kr−1

if r < r

0

E(h |Gr ) − E(h |Gr−1 ) if r ≥ r. F

F

(11.32)

Substituting (11.32) into (11.31), we obtain 1 F F F M(a ) = E(h |Gr ) − E(h |Gr−1 ) I(r ≥ r) r K r−1 r r 1 F F E(h |Gr ) − E(h |Gr−1 ) = r I(r ≥ r) K r−1 r r 1 E(hF |Gr ) − E(hF |Gr−1 ) Kr−1 = K r−1 r F F E(h |Gr ) − E(h |Gr−1 ) = r

= E(hF |G∞ ) − E(hF |G0 ) = hF . The second representation (11.28) will follow if we can show that 1+

λr 1 = . Kr ∞

(11.33)

r=∞

Substituting (11.29) into (11.33), we obtain 1 1 1 1 1 1 =1+ 1+ − − = = . Kr Kr−1 K K0 K ∞ r=∞

M−1 with Right Censored Data Because of the correspondence that was developed between monotone coarsening and right-censored data in Section 9.3, we immediately obtain the following result, which was ﬁrst given by Robins and Rotnitzky (1992). Lemma 11.2. In a survival analysis problem, full data are represented as ¯ )}. Using the notation of Section 9.3, we obtain that {T, X(T ¯ )}] M−1 [hF {T, X(T ¯ ¯ )} T λC˜ {r, X(r)} hF {T, X(T F ¯ ¯ = E h {T, X(T )} T ≥ r, X(r) − dr, ¯ )} ¯ KT {X(T Kr {X(r)} 0

11.2 Strategy for Obtaining Improved Estimators

285

or ¯ )}] = hF {T, X(T ¯ )}+ M−1 [hF {T, X(T T ¯ λC˜ {r, X(r)} ¯ )} − E hF {T, X(T ¯ )} T ≥ r, X(r) ¯ hF {T, X(T dr, ¯ Kr {X(r)} 0 ¯ ¯ was deﬁned in (9.30) and Kr {X(r)} was deﬁned in (9.33). where λC˜ {r, X(r)}

11.2 Strategy for Obtaining Improved Estimators The goal of this section is to outline a method for obtaining an AIPWCC estimator for β that is as eﬃcient as possible while still remaining as robust to model misspeciﬁcation as possible. Many of the calculations necessary to derive conditional expectations, projections, linear operators, etc., involve the coarsening probabilities as well as the marginal distribution of the full-data Z. In Chapter 10, we discussed methods for positing models for the coarsening probabilities and the distribution of Z in terms of ψ and ξ and ﬁnding estimators for these parameters. We ﬁrst consider ﬁnding a full-data estimating function m(Z, β) such that F m(Z, β0 ) = ϕ∗F (Z), where ϕ∗F (Z) ∈ ΛF ⊥ is an approximation to Beﬀ (Z). Because in general it is too diﬃcult to ﬁnd such an estimating function explicitly, we may instead use successive approximations as given by (11.48) and F (11.26). That is, we start with m(0) (Z, β) such that m(0) (Z, β0 ) = Seﬀ (Z) and iteratively compute D(i) (Z, β, ψˆn , ξˆn∗ ) = Π[(I − M)D(i−1) (Z, β, ψˆn , ξˆn∗ )|ΛF ] + m(0) (Z, β), (11.34) where we index D(·) by ψ and ξ to make clear that we need these parameters when computing the projection Π[(·)|ΛF ] and the linear operator M(·). After, say, j iterations, we compute m(Z, β, ψˆn , ξˆn∗ ) = Π[M{D(j) (Z, β, ψˆn , ξˆn∗ )}|ΛF ⊥ ]

(11.35)

F to serve as an approximation to Beﬀ (Z). Now that we’ve computed m(Z, β, ψˆn , ξˆn∗ ), where m(Z, β0 , ψ ∗ , ξ ∗ ) ∈ ΛF ⊥ , Λ2 . If we we need to compute in accordance with (11.16), Π I(C=∞)m(Z,β) (∞,Z)

have two levels of coarsening or monotone coarsening, then such a projection can be deﬁned explicitly, and in Chapter 10 we discussed how such projections can be obtained using adaptive methods. With nonmonotone coarsened data, we may proceed as follows. In Chapter 10, equation (10.95), we showed that Π equals

I(C=∞)m(Z,β) (∞,Z)

Λ2

286

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

⎡ ⎤ I(C = ∞) ⎣ − {r, Gr (Z)}E{dF (Z)|Gr (Z)}⎦ (∞, Z) r=∞ + I(C = r)E{dF (Z)|Gr (Z)},

(11.36)

r=∞

where dF (Z) = M−1 {m(Z, β)}. Since D(j) (Z, β, ψˆn , ξˆn∗ ) is an approximation to M−1 {m(Z, β, ψˆn , ξˆn∗ )}, we propose the following adaptive estimating equation: n I(Ci = ∞)m(Zi , β, ψˆn , ξˆn∗ ) (∞, Zi , ψˆn ) i=1 ⎡ ⎤ I(Ci = ∞) ⎣ {r, Gr (Zi ), ψˆn }E{D(j) (Z, β, ψˆn , ξˆn∗ )|Gr (Zi ), ξˆn∗ }⎦ − (∞, Zi , ψˆn ) r=∞ (j) ∗ ∗ ˆ ˆ ˆ + I(Ci = r)E{D (Z, β, ψn , ξn )|Gr (Zi ), ξn } = 0. (11.37) r=∞

The important thing to notice is that, by construction, m(Z, β0 , ψ ∗ , ξ ∗ ) ∈ ΛF ⊥ and ⎡

⎤

I(C = ∞) ⎣ {r, Gr (Z), ψ0 }E{D(j) (Z, β, ψ0 , ξ ∗ )|Gr (Z), ξ ∗ }⎦ (∞, Z, ψ0 ) r=∞ + I(C = r)E{D(j) (Z, β, ψ0 , ξ ∗ )|Gr (Z), ξ ∗ } ∈ Λ2

−

r=∞

as long as the model for the coarsening probabilities is correctly speciﬁed. F In some cases, it may be possible to derive Beﬀ (Z) directly. We illustrate with an example. Example: Restricted Moment Model with Monotone Coarsening When the coarsening of the data is monotone, we showed in Theorem 11.2, equation (11.27), how to derive the inverse operator M−1 in closed form. Let us now examine how we would go about ﬁnding a locally eﬃcient estimator for β with monotonically coarsened data. Speciﬁcally, we will consider the restricted moment model, as the semiparametric theory for such a model has been studied thoroughly throughout this book. We remind the reader that, for the restricted moment model, the full data are given by

11.2 Strategy for Obtaining Improved Estimators

287

Z = (Y, X), and the model assumes that E(Y |X) = µ(X, β), or, equivalently, Y = µ(X, β) + ε,

where E(ε|X) = 0.

For this semiparametric full-data model, we also derived a series of results regarding the geometry of the full-data inﬂuence functions and full-data estimating functions. Speciﬁcally, we showed in (4.48) that all elements of ΛF ⊥ are given by A(X)ε, where A(X) is a conformable matrix of functions of X. We also showed in (4.44) that ' ( Π[hF (Z)|ΛF ⊥ ] = E hF (Z)εT |X V −1 (X)ε, (11.38) where V (X) = var(Y |X). The full-data eﬃcient score was given by (4.53), F Seﬀ (Z) = DT (X)V −1 (X)ε,

where D(X) =

(11.39)

∂µ(X, β) . ∂β T

Suppose the data are coarsened at random with a monotone coarsening pattern. An example with monotone missing longitudinal data was given in Example 1 in Section 9.2 and also studied further in Section 10.3, where double-robust estimators were proposed. The question is, how do we go about ﬁnding a locally eﬃcient estimator for β with a sample of monotonically coarsened data {Ci , GCi (Zi )}, i = 1, . . . , n? We ﬁrst develop a model for the coarsening probabilities in terms of a parameter ψ. Because in this example we are assuming that coarsening is monotone, it is convenient to develop models for the discrete hazards λr {Gr (Z), ψ}, r = ∞ and obtain estimators for ψ by maximizing (8.12). We denote these estimators by ψˆn . We also posit a simpler, possibly incorrect model for the full data Z = (Y, X), Z ∼ p∗Z (z, ξ), and obtain an estimator for ξ, say ξˆn∗ , by maximizing the observed-data likelihood; see, for example, (10.58), n ! i=1

p∗Gr

i

(Zi ) (gri , ξ).

This model also gives us an estimate for var(Y |X, ξˆn∗ ) = V (X, ξˆn∗ ). If the data are coarsened at random, then by (11.16) and Theorem 11.1, the eﬃcient observed-data score is given by F F (Z, β, ψ, ξ) (Z, β, ψ, ξ) I(C = ∞)Beﬀ I(C = ∞)Beﬀ Λ2 , (11.40) −Π (∞, Z, ψ) (∞, Z, ψ) F where Beﬀ (Z, β, ψ, ξ) ∈ ΛF ⊥ must satisfy

288

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

F F Π M−1 {Beﬀ (Z, β, ψ, ξ)}|ΛF ⊥ = Seﬀ (Z, β, ξ). F (Z, β, ψ, ξ) = A(X, β, ψ, ξ)ε(β), where For the restricted moment model, Beﬀ ε(β) = Y − µ(X, β), and the matrix A(X, β, ψ, ξ) is obtained by solving the equation F Π M−1 {A(X, β, ψ, ξ)ε(β)} |ΛF ⊥ = Seﬀ (Z, β, ξ),

which by (11.38) and (11.39) is equal to E M−1 {A(X, β, ψ, ξ)ε(β)} εT (β)|X, ξ V −1 (X, ξ)ε(β) = DT (X, β)V −1 (X, ξ)ε(β), or, equivalently, E M−1 {A(X, β, ψ, ξ)ε(β)}εT (β)|X, ξ = DT (X, β).

(11.41)

If, in addition, the coarsening is monotone, then using the results from Theorem 11.2, equation (11.27), we obtain M−1 {A(X, β, ψ, ξ)ε(β)} = −

A(X, β, ψ, ξ)ε(β) (∞, Y, X, ψ)

λr {Gr (Y, X), ψ} E{A(X, β, ψ, ξ)ε(β)|Gr (Y, X), ξ}. Kr {Gr (Y, X), ψ}

r=∞

Combining this with (11.41), we obtain %# A(X, β, ψ, ξ)ε(β) E (∞, Y, X, ψ)

$ & λr {Gr (Y, X), ψ} T E {A(X, β, ψ, ξ)ε(β)|Gr (Y, X), ξ} × ε (β) X, ξ − Kr {Gr (Y, X), ψ} r=∞

= DT (X, β), or ε(β)εT (β) X, ξ A(X, β, ψ, ξ)E (∞, Y, X, ψ) ( T λr {Gr (Y, X), ψ} ' − E E A(X, β, ψ, ξ)ε(β) Gr (Y, X), ξ ε (β) X, ξ Kr {Gr (Y, X), ψ}

r=∞

= DT (X, β).

(11.42)

Remarks (i) In general, equation (11.42) is diﬃcult to solve. We do, however, get a simpliﬁcation for problems where the covariates X are always observed.

11.2 Strategy for Obtaining Improved Estimators

289

For instance, this was the case in Example 1 of Section 9.2, which was further developed in Section 10.3, where the responses Y = (Y1 , . . . , Yl )T were longitudinal data intended to be measured at times t1 < . . . < tl but were missing for some subjects in the study in a monotone fashion due to patient dropout. For this example, the covariate X (treatment assignment) was always observed but some of the longitudinal measurements that made up Y were missing. The coarsening was described as Gr (Z) = (X, Y r ), where Y r = (Y1 , . . . , Yr )T , r = 1, . . . , l − 1. Equation (11.42) can now be written as ε(β)εT (β) X, ξ (∞, Y, X, ψ) λr (X, Y r , ψ) ' ( T r −A(X, β, ψ, ξ) E , ξ ε (β) X, ξ E ε(β) X, Y Kr (X, Y r , ψ)

A(X, β, ψ, ξ)E

r=∞

= DT (X, β). Therefore, the solution is given by A(X, β, ψ, ξ) = DT (X, β)V˜ −1 (X, β, ψ, ξ), where

ε(β)εT (β) X, ξ (∞, Y, X, ψ) ( T λr (X, Y r , ψ) ' r − E ε(β) X, Y , ξ ε (β) X, ξ . E Kr (X, Y r , ψ)

˜ V (X, β, ψ, ξ) = E

r=∞

(ii) Except for special cases, such as the example above, the equation for solving A(X) in (11.42) is generally a complicated integral equation. Approximate methods for solving such integral equations are given in Kress (1989). However, these computations may be so diﬃcult as not to be feasible in practice. (iii) In Chapter 12, we will give some approximate methods for obtaining improved estimators that although not locally eﬃcient do have increased eﬃciency and are easier to implement. Suppose we were able to overcome these numerical diﬃculties and obtain an approximate solution for A(X, β, ψ, ξ). Denoting this solution by Aimp (X, β, ψˆn , ξˆn∗ ) and going back to (11.40), we approximate the eﬃcient score by I(C = ∞)Aimp (X, β, ψˆn , ξˆn∗ ){Y − µ(X, β)} π(∞, Y, X, ψˆn ) # $ I(C = ∞)Aimp (X, β, ψˆn , ξˆn∗ ){Y − µ(X, β)} −Π Λ2 . π(∞, Y, X, ψˆn )

(11.43)

290

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

Because the coarsening is monotone, we can use (10.55) to estimate the projection onto Λ2 by −L2 {C, GC (Y, X), β, ψˆn , ξˆn∗ }, where L2 {C, GC (Y, X), β, ψˆn , ξˆn∗ } # $ I(C = r) − λr {Gr (Y, X), ψˆn }I(C ≥ r) × Kr {Gr (Y, X), ψˆn } r=∞ E Aimp (X, β, ψˆn , ξˆn∗ ){Y − µ(X, β)} Gr (Y, X), ξˆn .

(11.44)

Finally, the estimator for β is given as the solution to the estimating equation # n I(Ci = ∞)Aimp (Xi , β, ψˆn , ξˆn∗ ){Yi − µ(Xi , β)} (∞, Yi , Xi , ψˆn ) i=1 $ ∗ ˆ ˆ (11.45) + L2 {Ci , GCi (Yi , Xi ), β, ψn , ξn } = 0.

Some Brief Remarks Regarding Robustness The estimator for β given by the solution to equation (11.45) used Aimp (Xi , β, ψˆn , ξˆn∗ ){Yi − µ(Xi , β)}

(11.46)

to represent the full-data estimating function m(Zi , β). Strictly speaking, (11.46) is not a full-data estimating function, as it involves the parameter estimators ψˆn and ξˆn∗ . However, as we discussed in Remark 1 of Chapter 9, the solution to the estimating equation (11.45) had we substituted Aimp (Xi , β, ψ ∗ , ξ ∗ ){Yi − µ(Xi , β)} as m(Zi , β), where ψ ∗ and ξ ∗ are the limits (in probability) of ψˆn and ξˆn∗ , would result in an asymptotically equivalent estimator for β. What is important to note here is that Aimp (X, β0 , ψ ∗ , ξ ∗ ){Y − µ(X, β0 )} ∈ ΛF ⊥

(11.47)

regardless of what the converging values ψ ∗ and ξ ∗ are, and therefore the solution to (11.45) is an example of an AIPWCC estimator for β. Because of (11.47), the estimator given as the solution to (11.45) with L2 (·) computed by (11.44) is an example of an improved estimator as described in Section 10.4. As such, this estimator is a double robust estimator in the sense that it will be consistent and asymptotically normal if either the coarsening model or the model for the posited marginal distribution of Z is correctly speciﬁed. This double-robustness property holds regardless of whether

11.3 Concluding Thoughts

291

F Aimp (X, β0 , ψ ∗ , ξ ∗ ){Y − µ(X, β0 )} = Beﬀ (Z)

or not. Finally, if both models are correctly speciﬁed, and if F Aimp (X, β0 , ψ0 , ξ0 ){Y − µ(X, β0 )} = Beﬀ (Z),

then the resulting estimator will be semiparametric eﬃcient. Thus, this methodology, assuming the numerical complexities could be overcome, would lead to locally eﬃcient observed-data estimators for β.

11.3 Concluding Thoughts In the last two chapters, we have outlined methods for obtaining increasingly eﬃcient estimators while trying to keep them as robust as possible. The key was always to use AIPWCC estimators. By no means do we want to give the impression that these methods are easily implemented. Deriving parameter estimates for ξ in a simpler posited parametric model for the marginal distribution of Z, which is used to obtain adaptive estimators, may require maximizing a coarsened-data likelihood. Such maximization algorithms may be complicated and may need specialized software. Even if these estimators are obtained, ﬁnding projections, deriving linear operators (such as M(·) or M−1 (·)) may require complicated integrals. Therefore, although the theory for improved adaptive estimation has been laid out, the actual implementation needs to be considered on a case-by-case basis. The use of this inverse weighted methodology can be thought of as a balance between simplicity of implementation and relative eﬃciency. The simplest estimator is the inverse probability weighted complete-case estimator based on some prespeciﬁed full-data estimating function. Since this estimator only uses complete cases, it may be throwing away a great deal of information from data that are coarsened. Depending on how much of the data are coarsened, this estimator may be inadequate. Also, the consistency of the simpler IPWCC estimator depends on correctly modeling the coarsening probabilities. Improving the performance of the estimator by augmentation while using the same full-data estimating function is the next step. To implement these methods, one needs to develop simpler and possibly incorrect models for the marginal distribution of Z to be used as part of an adaptive approach. This can improve the eﬃciency considerably but at the cost of increased computations and model building. The attempt to gain eﬃciency also gives you the extra protection of double robustness in that the resulting AIPWCC estimator will be consistent if either the model for the coarsening probabilities or the posited model for the marginal distribution of Z is correctly speciﬁed. Finally, the attempt to adaptively obtain the locally eﬃcient estimator is the most complex numerically. Here we actually attempt to ﬁnd the optimal

292

11 Locally Eﬃcient Estimators for Coarsened-Data Semiparametric Models

F full-data estimating function, Beﬀ (Z) ∈ ΛF ⊥ , as well as the optimal augmentation. It is not clear whether the eﬃciency gains of such an estimator would make such a complicated procedure attractive for practical use. Because of the complexity of these methods, we oﬀer in the next chapter some simpler methods for gaining eﬃciency that are easier to implement. These methods will not generally result in locally eﬃcient estimators, but they are, however, more feasible.

11.4 Recap and Review of Notation •

The observed-data eﬃcient score, which can be used to derive adaptive AIPWCC estimators that are locally eﬃcient, is given by F (Z)}, Seﬀ {C, GC (Z)} = J {Beﬀ

where I(C = ∞)hF (Z) I(C = ∞)hF (Z) −Π Λ2 , J {h (Z)} = (∞, Z) (∞, Z) F

F Beﬀ (Z) is the unique element in ΛF ⊥ that satisﬁes F F (Z)}|ΛF ⊥ ] = Seﬀ (Z), Π[M−1 {Beﬀ

M−1 (·) is the inverse of the linear operator M{hF (Z)} = E[E{hF (Z)|C, GC (Z)}|Z] = {r, Gr (Z)}E{hF (Z)|Gr (Z)}, r

•

F and Seﬀ (Z) is the full-data eﬃcient score vector. F We can derive Beﬀ (Z) by solving the equation F F (Z)} = Seﬀ (Z), (I − Q)M−1 {Beﬀ

where (I − Q)(·) is a linear operator, with Q(·) deﬁned as Q{hF (Z)} = Π[(I − M){hF (Z)} ΛF ]. (I − Q)(·) is a contraction mapping and hence has a unique inverse. ThereF F F F (Z) = M{Deﬀ (Z)}, where Deﬀ (Z) = (I − Q)−1 {Seﬀ (Z)}. The fore, Beﬀ F solution Deﬀ (Z) can be obtained by successive approximation, F (Z), (11.48) D(i+1) (Z) = Π (I − M)D(i) (Z) ΛF + Seﬀ and

F (Z). D(i) (Z) −−−→ Deﬀ i→∞

11.5 Exercises for Chapter 11

If we deﬁne

293

B (i) (Z) = Π M{D(i) (Z)} ΛF ⊥ ,

where, by construction, B (i) (Z) ∈ ΛF ⊥ , then F (Z). B (i) (Z) −−−→ Beﬀ i→∞

11.5 Exercises for Chapter 11 1. Recall that, with two levels of missingness, the data Z = (Z1T , Z2T )T , where Z1 is always observed and Z2 may be missing. We denote by R the complete-case indicator and assume P (R = 1|Z) = P (R = 1|Z1 ) = π(Z1 ) (i.e., MAR). In Theorem 11.2, we derived the inverse operator M−1 when coarsening is monotone. You should derive an explicit expression for M−1 when there are two levels of missingness. (Note: Two levels of missingness can be viewed as a special case of monotone coarsening.) 2. In Section 11.2, we outlined the steps necessary to obtain a locally eﬃcient estimator for β in a restricted moment model, E(Y |X) = µ(X, β), when the data are monotonically coarsened. Similarly, outline the steps necessary to obtain a locally eﬃcient estimator for β if there are two levels of missingness.

12 Approximate Methods for Gaining Eﬃciency

12.1 Restricted Class of AIPWCC Estimators In Chapters 10 and 11, we described various methods to increase the eﬃciency of AIPWCC estimators. Although feasible in some situations, these methods are often computationally very challenging. For example, to use these methods, one needs to posit simpler models for the marginal distribution of the full-data Z in terms of a parameter ξ (i.e., p∗Z (z, ξ)) and then estimate the parameter. This in itself can be a challenging numerical problem. But, even if estimators for ξ can be derived, it is necessary to compute a series of conditional expectations that might involve complicated integrals that are diﬃcult to compute numerically. Moreover, as discussed in Chapter 11, an attempt to derive locally eﬃcient estimators could result in complicated integral equations that are diﬃcult to solve. Therefore, in this chapter, we explore other methods that are numerically easier to implement but will result in gains in eﬃciency. We remind the reader that all semiparametric observed-data RAL estimators for β, when the coarsening is CAR, are asymptotically equivalent to an AIPWCC estimator, which is the solution to " n I(Ci = ∞)m(Zi , β) (12.1) + L2 {Ci , GCi (Zi ), ψˆn } = 0, (∞, Zi , ψˆn ) i=1 where the estimating function m(Z, β) is chosen so that m(Z, β0 ) = ϕ∗F (Z) ∈ ΛF ⊥ ⊂ HF and L2 (·) ∈ Λ2 ⊂ H, and ψˆn denotes the MLE for ψ obtained by maximizing n ! {Ci , GCi (Zi ), ψ}. i=1

Rather than searching for the optimal AIPWCC estimator, which involves F ﬁnding the optimal L2eﬀ (·) ∈ Λ2 and the optimal Beﬀ (Z) ∈ ΛF ⊥ , we instead restrict the search to linear subspaces of Λ2 ⊂ H and ΛF ⊥ ⊂ HF . That is,

296

12 Approximate Methods for Gaining Eﬃciency

we will only consider AIPWCC estimators that are the solution to (12.1) for m(Z, β0 ) = ϕ∗F (Z) ∈ G F and L2 (·) ∈ G2 , where G F is a q-replicating linear subspace of ΛF ⊥ and G2 is a q-replicating linear subspace of Λ2 , where a q-replicating linear space is deﬁned by Deﬁnition 6 of Chapter 3. Remark 1. We remind the reader that a full-data Hilbert space HF consists of mean-zero ﬁnite-variance q-dimensional functions of Z and the observed-data Hilbert space H consists of mean-zero ﬁnite-variance q-dimensional functions of {C, GC (Z)}. In Deﬁnition 6 of Chapter 3, we noted that a q-replicating linear subspace can be written as {U (1) }q , where U (1) is a linear subspace contained in the Hilbert space H(1) of one-dimensional mean-zero ﬁnite-variance functions and where h(·) = {h1 (·), . . . , hq (·)}T is deﬁned to be an element of {U (1) }q if and only if each element hj (·) ∈ U (1) , j = 1, . . . , q. Linear subspaces, such as ΛF ⊥ ⊂ HF and Λ2 ⊂ H, are examples of q-replicating spaces. The importance of deﬁning q-replicating linear spaces is given by Theorem 3.3, which allows a generalization of the Pythagorean theorem to q dimensions. One of the consequences of Theorem 3.3 is that if an element h is orthogonal to a q-replicating linear space, say {U (1) }q , then not only is E(hT u) = 0 for all u ∈ {U (1) }q (the deﬁnition of orthogonality) but also E(huT ) = 0q×q for all u ∈ {U (1) }q .

(12.2)

We will consider two speciﬁc classes of restricted estimators. 1. For the ﬁrst class of restricted estimators, we will take both G F ⊂ ΛF ⊥ and G2 ⊂ Λ2 to be ﬁnite-dimensional linear subspaces. 2. For the second class of restricted estimators, we will take G F ⊂ ΛF ⊥ to be a ﬁnite-dimensional linear subspace contained in the orthogonal complement of the full-data nuisance tangent space but will let G2 = Λ2 be the entire augmentation space. We note that a ﬁnite-dimensional linear subspace G F of ΛF ⊥ can be deﬁned by choosing a t1 -dimensional function of Z, say J F (Z) = {J1F (Z), . . . , JtF1 (Z)}T , (1) where JjF (Z) ∈ ΛF ⊥ , j = 1, . . . , t1 , and letting the space spanned by J F (Z) be G F = Aq×t1 J F (Z) for all constant matrices Aq×t1 . Similarly, for (class 1) restricted estimators, a ﬁnite-dimensional linear subspace G2 of Λ2 can be deﬁned by choosing a t2 -dimensional function of {C, GC (Z)}, say J2 {C, GC (Z)} = [J21 {C, GC (Z)}, . . . , J2t2 {C, GC (Z)}]T , where (1) J2j {C, GC (Z)} ∈ Λ2 , j = 1, . . . , t2 , and letting the space spanned by J2 {C, GC (Z)} be q×t2 q×t2 . G2 = A J2 {C, GC (Z)} for all constant matrices A

12.1 Restricted Class of AIPWCC Estimators

297

We will always assume that the t1 elements of J F (Z) and the t2 elements of J2 {C, GC (Z)} are linearly independent; that is, cT J F (Z) = 0 implies that c = 0, where c is a t1 vector of constants (similarly for J2 {C, GC (Z)}). By construction, the ﬁnite-dimensional linear spaces deﬁned above are q-replicating linear spaces. Therefore, we will restrict attention to estimators for β that are the solution to the estimating equation # $ q×t1 n I(Ci = ∞)AF m∗ (Zi , β) ˆ (12.3) + L2 {Ci , GCi (Zi ), ψn } = 0, (∞, Zi , ψˆn ) i=1 where m∗ (Z, β) is a t1 -dimensional estimating function such that m∗ (Z, β0 ) = q×t1 J F (Z) and AF is an arbitrary q×t1 constant matrix, and L2 (·) ∈ G2 , either 2 2 L2 {C, GC (Z)} = Aq×t J2 {C, GC (Z)}, where Aq×t is an arbitrary q×t2 matrix 2 2 (class 1), or L2 (·) ∈ Λ2 (class 2). Remark 2. The observed-data estimating function (12.1) uses the full-data estimating function m(Z, β) to build estimators. Because the parameter β is q-dimensional, the estimating function m(Z, β) is also q-dimensional, and, at the minimum, the elements of m(Z, β) must be linearly independent. When we consider restricted estimators that solve (12.3), the full-data esq×t1 m∗ (Z, β), where m∗ (Z, β) timating function m(Z, β) is now equal to AF is t1 -dimensional. To ensure that the elements of m(Z, β), constructed in this way, are linearly independent, the dimension t1 must be greater than or equal to q. Moreover, since estimating equations, such as (12.3), can be deﬁned up to a proportional constant matrix (that is, multiplying the left-hand side of (12.3) by a nonsingular q ×q matrix will not aﬀect the resulting estimator), we must choose the dimension t1 to be strictly greater than q so that the strategy of choosing from this class of restricted estimators has an eﬀect on the resulting estimator. Therefore, from here on, we will always assume that m∗ (Z, β), chosen so that m∗ (Z, β0 ) = J F (Z), is made up of t1 linearly independent elements with t1 > q. Let us deﬁne the q-replicating linear space Ξ ⊂ H to be I(C = ∞)G F Ξ= ⊕ G2 ; (∞, Z)

(12.4)

that is, Ξ consists of the elements I(C = ∞)AF m∗ (Z, β0 ) + L2 {C, GC (Z)} (∞, Z)

(12.5)

in H for any constant matrix AF and L2 ∈ G2 . We remind the reader that the orthogonal complement of the nuisance tangent space associated with the full-data nuisance parameter η is denoted by Λ⊥ η , which, by Theorem 7.2, is equal to

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12 Approximate Methods for Gaining Eﬃciency

Λ⊥ η = and hence

I(C = ∞)ΛF ⊥ ⊕ Λ2 , (∞, Z) Ξ ⊂ Λ⊥ η.

(12.6)

The elements in the linear subspace Ξ are associated with estimating functions that will lead to the restricted class of estimating equations (12.3) that we are considering. However, in Theorem 9.1, we showed that substituting the MLE ψˆn for the parameter ψ in an AIPWCC estimating equation resulted in an inﬂuence function that subtracts oﬀ a projection onto the space Λψ , where Λψ denotes the linear subspace spanned by the score vector Sψ {C, GC (Z)} =

∂ log {C, GC (Z), ψ0 } . ∂ψ

We remind the reader that when we introduce a coarsening model with parameter ψ, the nuisance tangent space is given by Λ = Λη ⊕ Λψ . Since estimating functions need to be associated with elements in Λ⊥ , we should only consider elements of Ξ that are also orthogonal to Λψ ; i.e., Π[Ξ|Λ⊥ ψ ]. Consequently, it will prove desirable that the space G2 ⊂ Λ2 also contain Λψ . For the restricted (class 2) estimators where G2 = Λ2 , this is automatically true, however, for the restricted (class 1) estimators, we will always include the elements Sψ {C, GC (Z)} that span Λψ as part of the vector J2 {C, GC (Z)} that spans G2 to ensure that Λψ ⊂ G2 . Because the variance of an RAL estimator for β is the variance of its inﬂuence function, when we consider ﬁnding the optimal estimator within the restricted class of estimators (12.3), then we are looking for the estimator whose inﬂuence function has the smallest variance matrix. Recall that an inﬂuence function in addition to being orthogonal to the nuisance tangent space must also satisfy the property that E[ϕ{C, GC (Z)}SβT {C, GC (Z)}] = E[Sβ {C, GC (Z)}ϕT {C, GC (Z)}] = I q×q ,

(12.7)

where Sβ {C, GC (Z)} is the observed-data score vector with respect to β, which also equals the conditional expectation of the full-data score vector with respect to β given the observed data (i.e., Sβ {C, GC (Z)} = E{SβF (Z)|C, GC (Z)}), and ϕ{C, GC (Z)} denotes an inﬂuence function. One can always normalize any element ϕ∗ {C, GC (Z)} ∈ Ξ to ensure that it satisﬁes (12.7) by choosing −1 ∗ T ϕ{C, GC (Z)} = E[ϕ {C, GC (Z)}Sβ {C, GC (Z)}] ϕ∗ {C, GC (Z)}. (12.8) Also, because Ξ is a q-replicating linear subspace, the element ϕ{C, GC (Z)} ∈ Ξ. Let us deﬁne the subset of elements ϕ{C, GC (Z)} ∈ Ξ that satisfy the property (12.7) or, equivalently, the subset of elements deﬁned by (12.8), as

12.1 Restricted Class of AIPWCC Estimators

299

IF (Ξ). In Theorem 12.1 below, we derive the optimal inﬂuence function within IF (Ξ); i.e., the element within IF (Ξ) that has the smallest variance matrix. Remark 3. If the coarsening probabilities are modeled using a parameter ψ that needs to be estimated, then inﬂuence functions of RAL estimators for β must be orthogonal to Λψ . However, as we will show shortly, as long as Λψ ⊂ G2 , then the optimal inﬂuence function within IF (Ξ) will also be orthogonal to Λψ , as is desired. Theorem 12.1. Among the elements ϕ{C, GC (Z)} ∈ IF (Ξ), the one with the smallest variance matrix is given by the normalized version of the projection of Sβ {C, GC (Z)} onto Ξ. Speciﬁcally, if we deﬁne ϕ∗opt {C, GC (Z)} = Π[Sβ {C, GC (Z)}|Ξ], then the element in IF (Ξ) with the smallest variance matrix is given by ϕopt {C, GC (Z)} =

−1

E[ϕ∗opt {C, GC (Z)}SβT {C, GC (Z)}]

ϕ∗opt {C, GC (Z)}. (12.9)

Proof. Because Ξ is a closed linear subspace, then, by the projection theorem for Hilbert spaces, there exists a unique projection ϕ∗opt {C, GC (Z)} = Π[Sβ {C, GC (Z)}|Ξ] such that the residual Sβ {C, GC (Z)}−ϕ∗opt {C, GC (Z)} is orthogonal to every element in Ξ. Consider any element ϕ{C, GC (Z)} ∈ IF (Ξ). Because IF (Ξ) ⊂ Ξ and, since both ϕ{C, GC (Z)} and ϕopt {C, GC (Z)} belong to IF (Ξ), this implies that ϕ(·)−ϕopt (·) ∈ Ξ. Also, because Ξ is a q-replicating linear space (see Remark 1), by Theorem 3.3 we obtain that E[{Sβ (·) − ϕ∗opt (·)}{ϕ(·) − ϕopt (·)}T ] = 0q×q . Because elements of IF (Ξ) must satisfy (12.7), this implies that E{Sβ (·)ϕT (·)} = E{Sβ (·)ϕTopt (·)} = I q×q , and hence

E[ϕ∗opt (·){ϕ(·) − ϕopt (·)}T ] = 0. −1 Premultiplying by the constant matrix E{ϕ∗opt (·)SβT (·)} , we obtain E[ϕopt (·){ϕ(·) − ϕopt (·)}T ] = 0. Consequently, E{ϕ(·)ϕT (·)} = E[{ϕopt (·) + ϕ(·) − ϕopt (·)}{ϕopt (·) + ϕ(·) − ϕopt (·)}T ] = E{ϕopt (·)ϕTopt (·)} + E[{ϕ(·) − ϕopt (·)}{ϕ(·) − ϕopt (·)}T ] + 0.

300

12 Approximate Methods for Gaining Eﬃciency

Since E[{ϕ(·) − ϕopt (·)}{ϕ(·) − ϕopt (·)}T ] is a nonnegative deﬁnite matrix, this implies that E{ϕ(·)ϕT (·)} ≥ E{ϕopt (·)ϕTopt (·)}, giving us the desired result. Corollary 1. The optimal element ϕopt {C, GC (Z)} ∈ IF (Ξ), derived in Theorem 12.1, is orthogonal to Λψ and is an element of the space of observed-data inﬂuence functions (IF ). Proof. By construction, Sβ {C, GC (Z)} − ϕ∗opt {C, GC (Z)} is orthogonal to Ξ, where Ξ is deﬁned by (12.4). Hence, it must be orthogonal to G2 . Also, by construction, Λψ ⊂ G2 . This implies that Sβ {C, GC (Z)} − ϕ∗opt {C, GC (Z)} is orthogonal to Λψ . In other words, E{(Sβ − ϕ∗opt )T h} = 0 for all h ∈ Λψ . But since Sβ {C, GC (Z)} is orthogonal to Λψ (see equation (11.7)), this implies that ϕ∗opt {C, GC (Z)} must be orthogonal to Λψ . Therefore ϕopt {C, GC (Z)}, deﬁned by (12.9), is also orthogonal to Λψ . Hence, ⊥ ⊥ ⊥ ϕopt {C, GC (Z)} ∈ Π[Ξ|Λ⊥ ψ ] ⊂ Π[Λη |Λψ ] = Λ ,

where the last two relationships follow from (12.6) and (8.16), respectively. Therefore, ϕopt {C, GC (Z)} is an element orthogonal to the observed-data nuisance tangent space and by construction (see (12.9)) satisﬁes (12.7). Hence, ϕopt {C, GC (Z)} is an element of the space of observed-data inﬂuence functions (IF ).

12.2 Optimal Restricted (Class 1) Estimators We ﬁrst consider how we can use the result from Theorem 12.1 to obtain improved estimators by ﬁnding the optimal estimator within the restricted 2 class of estimators (12.3) where L2 {C, GC (Z)} = Aq×t J2 {C, GC (Z)}; i.e., the 2 so-called (class 1) estimators. For this class of estimators, we will assume that the model for the coarsening probabilities has been correctly speciﬁed. Examining the elements in (12.5), we note that Ξ ⊂ H is a ﬁnitedimensional linear subspace that is spanned by the (t1 +t2 ) vector of functions of the observed data, namely "T T I(C = ∞)m∗ (Z, β0 ) T . , J2 (·) (∞, Z) Therefore, ﬁnding the projection onto this linear subspace of H is exactly the same as Example 2 of Chapter 2, which was solved by using equation (2.2). Applying this result to our problem, ϕ∗opt (·) of Theorem 12.1 is obtained by

12.2 Optimal Restricted (Class 1) Estimators

301

q×t1 ﬁnding the constant matrices (AF and (A2opt )q×t2 that solve the linear opt ) equation ⎡⎧ ⎡ ⎤(t1 +t2 )×1 ⎫ ∗ ⎪ ⎪ (Z, β ) I(C = ∞)m ⎨ ⎬ 0 F q×(t1 +t2 ) ⎢ ⎣ ⎦ E ⎣ Sβ (·) − Aopt , A2opt (∞, Z) ⎪ ⎪ ⎩ ⎭ J2 (·) ⎤ ⎧# $1×(t1 +t2 ) ⎫ ⎨ I(C = ∞)m∗T (Z, β ) ⎬ ⎥ 0 q×(t1 +t2 ) × , J2T (·) . ⎦=0 ⎩ ⎭ (∞, Z)

(12.10) Before deriving the solution to equation (12.10), we ﬁrst give some results through a series of lemmas that will simplify the equation. Lemma 12.1. T T I(C = ∞)m∗ (Z, β0 )AF E Sβ {C, GC (Z)} (∞, Z) T T F = E Sβ (Z)m∗ (Z, β0 )AF T ∂m∗ (Z, β0 ) F =− A E . ∂β T

(12.11) (12.12)

Proof. We prove (12.11) using a series of iterated conditional expectations, where $ T T I(C = ∞)m∗ (Z, β0 )AF E Sβ {C, GC (Z)} (∞, Z) # $ T T ' F ( I(C = ∞)m∗ (Z, β0 )AF = E E Sβ (Z)|C, GC (Z) (∞, Z) # "$ T T I(C = ∞)m∗ (Z, β0 )AF F = E E Sβ (Z) C, GC (Z) (∞, Z) " ∗T FT (Z, β )A I(C = ∞)m 0 = E SβF (Z) (∞, Z) # "$ T T I(C = ∞)m∗ (Z, β0 )AF F = E E Sβ (Z) Z (∞, Z) # "$ ∗T FT (Z, β )A I(C = ∞)m 0 = E SβF (Z)E Z (∞, Z) T T = E SβF (Z)m∗ (Z, β0 )AF . #

302

12 Approximate Methods for Gaining Eﬃciency

Equation (12.12) follows from the usual expansion for m-estimators, where the inﬂuence function for the estimator that solves the equation n

AF m∗ (Zi , β) = 0

i=1

has inﬂuence function −1 ∂m∗ (Z, β0 ) F ϕ (Z) = − A E AF m∗ (Z, β0 ). ∂β T F

T

Because E{ϕF (Z)SβF (Z)} = I q×q , this implies that F

∗

E A m

T (Z, β0 )SβF (Z)

= −A E F

∂m∗ (Z, β0 ) ∂β T

.

The result in (12.12) now follows after taking the transpose of both sides of the equation above. Lemma 12.2. E Sβ {C, GC (Z)}J2T {C, GC (Z)} = 0q×t2 .

(12.13)

Proof. Using a series of iterated conditional expectations similar to the proof of Lemma 12.1, we obtain that E Sβ {C, GC (Z)}J2T {C, GC (Z)} F T = E Sβ (Z)E J2 {C, GC (Z)} Z . Because each of the elements of J2 {C, GC (Z)} is an element of Λ2 , this implies that E J2 {C, GC (Z)} Z = 0, thus giving us the desired result. Lemma 12.3. " T I(C = ∞) ∗ m∗ (Z, β0 )m∗ (Z, β0 ) ∗T . E m (Z, β0 )m (Z, β0 ) = E 2 (∞, Z) (∞, Z) (12.14) Proof. This follows easily by an iterated conditional expectation argument where we ﬁrst compute the conditional expectation given Z.

12.2 Optimal Restricted (Class 1) Estimators

as

303

Using the results from Lemmas 12.1–12.3, we obtain the solution to (12.10) $ # U U 11 12 [AF = [H1 , H2 ], (12.15) opt , A2opt ] T U12 U22

where

U11 = E

T

m∗ (Z, β0 )m∗ (Z, β0 ) (∞, Z)

"t1 ×t1 ,

t1 ×t2 I(C = ∞) ∗ T U12 = E m (Z, β0 )J2 {C, GC (Z)} , (∞, Z) ' (t2 ×t2 , U22 = E J2 {C, GC (Z)}J2T {C, GC (Z)} T q×t1 ∗ ∂m (Z, β0 ) H1 = − E , ∂β T H2 = 0q×t2 .

(12.16)

Therefore, solving (12.15) yields # [AF opt , A2opt ]

= [H1 , 0] # = [H1 , 0]

U11 U12

$−1

T U12 U22

U 11 U 12

$

U 12T U 22

= [H1 U 11 , H1 U 12 ]. Using standard results for the inverse of partitioned symmetric matrices (see, for example, Rao, 1973, p.33), we obtain

and where and

q×t1 11(t1 ×t1 ) U AF opt = H1

(12.17)

A2opt = H1q×t1 U 12(t1 ×t2 ) ,

(12.18)

−1 T −1 U12 U 11 = U11 − U12 U22

(12.19)

−1 . U 12 = −U 11 U12 U22

(12.20)

Thus we have shown that the optimal inﬂuence function ϕopt {C, GC (Z)} in IF (Ξ), given by Theorem 12.1, is obtained by choosing ϕ∗ {C, GC (Z)} ∈ Ξ to be ϕ∗opt {C, GC (Z)} =

∗ I(C = ∞)AF opt m (Z, β0 ) + A2opt J2 {C, GC (Z)}, (∞, Z)

(12.21)

where AF opt and A2opt are deﬁned by (12.17) and (12.18), respectively. We also note the following interesting relationship.

304

12 Approximate Methods for Gaining Eﬃciency I(C=∞)AF m∗ (Z,β0 )

opt Lemma 12.4. The projection of onto the space G2 (i.e., (∞,Z) the space spanned by J2 {C, GC (Z)}) is equal to ∗ I(C = ∞)AF opt m (Z, β0 ) G2 = −A2opt J2 {C, GC (Z)}, (12.22) Π (∞, Z)

which implies that ϕ∗opt {C, GC (Z)} =

∗ I(C = ∞)AF opt m (Z, β0 ) + A2opt J2 {C, GC (Z)} (∞, Z)

is orthogonal to G2 ; that is, ϕ∗opt {C, GC (Z)} ⊥ G2 .

(12.23)

Proof. Because G2 is a linear subspace spanned by J2 {C, GC (Z)}, the projection (12.22) is given by # $ ∗ I(C = ∞)AF opt m (Z, β0 ) T E J2 {C, GC (Z)} (∞, Z) −1 × E[J2 {C, GC (Z)}J2T {C, GC (Z)}] J2 {C, GC (Z)} −1 −1 11 = AF U J2 {C, GC (Z)} U U J {C, G (Z)} = H U U 12 2 C 1 12 opt 22 22 = −H1 U 12 J2 {C, GC (Z)} = −A2opt J2 {C, GC (Z)}.

The optimal constant matrices AF opt and A2opt involve the quantities H1 , U11 , U12 , U22 , which are all matrices whose elements are expectations. For practical applications, these must be estimated from the data. We propose the following empirical averages: T n I(Ci = ∞) ∂m∗ (Zi , β) ˆ 1 (β) = − n−1 H , ˆ ∂β T i=1 (∞, Zi , ψn ) ˆ11 (β) = n−1 U

n i=1

ˆ12 (β) = n−1 U

n I(Ci = ∞) m∗ (Zi , β)J2T {Ci , GCi (Zi ), ψˆn } , (∞, Zi , ψˆn ) i=1

and

I(Ci = ∞) ∗ ∗T m (Zi , β)m (Zi , β) , 2 (∞, Zi , ψˆn )

(12.24)

(12.25)

(12.26)

12.2 Optimal Restricted (Class 1) Estimators

ˆ22 = n−1 U

n

J2 {Ci , GCi (Zi ), ψˆn }J2T {Ci , GCi (Zi ), ψˆn }.

305

(12.27)

i=1

Consequently, we would estimate ˆ ˆ ˆ 11 ˆ ˆ 12 [AˆF opt (β), A2opt (β)] = [H1 (β)U (β), H1 (β)U (β)],

(12.28)

ˆ 11 (β) and U ˆ 12 (β) are obtained by substituting the empirical estimates where U for U11 , U12 , and U22 into equations (12.19) and (12.20). Deriving the Optimal Restricted (Class 1) AIPWCC Estimator Using a sample of observed data {Ci , GCi (Zi )}, i = 1, . . . , n, we propose estimating β by solving the equation # $ n ∗ I(Ci = ∞)AˆF opt (β)m (Zi , β) + Aˆ2opt (β)J2 {Ci , GCi (Zi ), ψˆn } = 0. (∞, Zi , ψˆn ) i=1

(12.29) We denote this estimator as βˆnopt and now prove the fundamental result for restricted optimal estimators. Theorem 12.2. Among the restricted class of AIPWCC estimators (12.3), the optimal estimator (i.e., the estimator with the smallest asymptotic variance matrix) is given by βˆnopt , the solution to (12.29). Before sketching out the proof of Theorem 12.2, we give another equivalent representation for the class of inﬂuence functions IF (Ξ) deﬁned by (12.8) that will be useful. Lemma 12.5. The class of inﬂuence functions IF (Ξ) can also be deﬁned by ϕ{C, GC (Z)} =

− AF E

∂m∗ (Z, β0 ) ∂β T

−1

ϕ∗ {C, GC (Z)},

(12.30)

where ϕ∗ {C, GC (Z)} is an element of Ξ deﬁned by (12.5). Proof. Using (12.12) of Lemma 12.1 and Lemma 12.2, we can show that ∂m∗ (Z, β0 ) ∗ T F . (12.31) E[ϕ {C, GC (Z)}Sβ {C, GC (Z)}] = −A E ∂β T The lemma now follows by substituting the right-hand side of (12.31) into (12.8). Proof. Theorem 12.2 Since the asymptotic variance of an RAL estimator is the variance of its inﬂuence function, it suﬃces to consider the inﬂuence functions of the competing

306

12 Approximate Methods for Gaining Eﬃciency

estimators. In the same manner that we found the inﬂuence function of the estimator in (9.7) of Theorem 9.1, we can show that the inﬂuence function of the estimator for β that solves (12.3) is given by −1 ∂m∗ (Z, β0 ) F ϕ{C, GC (Z)} = − A E ∂β T F ∗ I(C = ∞)A m (Z, β0 ) + A2 J2 {C, GC (Z)} − Π · Λψ . (12.32) × (∞, Z) Because we constructed the space G2 so that Λψ ⊂ G2 , this implies that Π[[·]|Λψ ] ∈ G2 , which in turn implies that (12.32) is an element of Ξ. Therefore, as a consequence of Lemma 12.5, the inﬂuence function ϕ{C, GC (Z)} deﬁned above is an element of IF (Ξ). By Theorem 12.1, we know that var[ϕopt {C, GC (Z)} ≤ var[ϕ{C, GC (Z)}]. Hence, if we can show that the inﬂuence function of the estimator (12.29) is equal to ϕopt {C, GC (Z)}, then we would complete the proof of the theorem. An expansion of the estimating equation in (12.29) about β = β0 , keeping ψˆn ﬁxed, yields −1 ∂m∗ (Z, β0 ) n1/2 (βˆnopt − β0 ) = − AF E × opt ∂β T # $ n ∗ I(Ci = ∞)AˆF opt (β0 )m (Zi , β0 ) −1/2 ˆ ˆ n + A2opt (β0 )J2 {Ci , GCi (Zi ), ψn } (∞, Zi , ψˆn ) i=1 + op (1).

(12.33)

We now show that estimating AF opt and A2opt only has a negligible eﬀect on the asymptotic properties of the estimator by noting that (12.33) equals # $ n ∗ I(Ci = ∞)AF opt m (Zi , β0 ) −1/2 n (12.34) + A2opt J2 {Ci , GCi (Zi ), ψˆn } (∞, Zi , ψˆn ) i=1

F −1 + n1/2 {AˆF opt (β0 ) − Aopt }n

+ n1/2 {Aˆ2opt (β0 ) − A2opt }n

n I(Ci = ∞)m∗ (Zi , β0 ) (∞, Zi , ψˆn )

(12.35)

J2 {Ci , GCi (Zi ), ψˆn }.

(12.36)

i=1 n −1 i=1

F 1/2 ˆ {A2opt (β0 )− Under mild regularity conditions, n1/2 {AˆF opt (β0 )−Aopt } and n A2opt } will be bounded in probability, n I(Ci = ∞)m∗ (Zi , β0 ) P I(C = ∞)m∗ (Z, β0 ) −1 − →E n (∞, Zi , ψ0 ) (∞, Zi , ψˆn ) i=1

= E{m∗ (Z, β0 )} = 0,

12.2 Optimal Restricted (Class 1) Estimators

and n−1

n

307

P

J2 {Ci , GCi (Zi ), ψˆn } − → E[J2 {C, GC (Z), ψ0 }] = 0.

i=1

Hence (12.35) and (12.36) will converge in probability to zero. Using Theorem 9.1, we can expand ψˆn about ψ0 in (12.34) to obtain that (12.34) equals n ∗ I(Ci = ∞)AF opt m (Zi , β0 ) + A2opt J2 {Ci , GCi (Zi ), ψ0 } n−1/2 (∞, Zi , ψ0 ) i=1 ∗ I(Ci = ∞)AF opt m (Zi , β0 ) −Π + A2opt J2 {Ci , GCi (Zi ), ψ0 } Λψ (∞, Zi , ψ0 ) + op (1).

(12.37)

Combining all the results from (12.33) through (12.37), we obtain that the inﬂuence function of βˆnopt , the solution to (12.29), is given by

−

AF opt E

∂m∗ (Z, β0 ) ∂β T

−1

ϕ∗opt {C, GC (Z)}

−

Π[ϕ∗opt {C, GC (Z)}|Λψ ]

,

(12.38) where ϕ∗opt {C, GC (Z)} is deﬁned by (12.21). We proved that ϕ∗opt {C, GC (Z)} is orthogonal to Λψ in Corollary 1. We also demonstrated in (12.23) of Lemma 12.4 that ϕ∗opt {C, GC (Z)} is orthogonal to G2 . Since Λψ ⊂ G2 , this, too, implies that Π[ϕ∗opt {C, GC (Z)}|Λψ ] = 0. Therefore, the inﬂuence function (12.38) is equal to −1 ∂m∗ (Z, β0 ) F ϕopt {C, GC (Z)} = − Aopt E ϕ∗opt {C, GC (Z)}, ∂β T

(12.39)

thus proving that the estimator that is the solution to (12.29) is the optimal restricted estimator. Estimating the Asymptotic Variance Using the matrix relationships (12.16) and (12.17), we note that the leading term in (12.39) can be written as the symmetric matrix −1 −1 ∂m∗ (Z, β0 ) 11 T − AF E = H U H . (12.40) 1 opt 1 ∂β T After a little algebra, we can also show that the covariance matrix of ϕ∗opt {C, GC (Z)} is equal to ∗ ∗T E ϕopt (·)ϕopt (·) = H1 U 11 H1T . (12.41)

308

12 Approximate Methods for Gaining Eﬃciency

Because the asymptotic variance of an RAL estimator is equal to the variance of its inﬂuence function, this means that the asymptotic variance of βˆnopt is the variance of (12.39). Using (12.40) and (12.41), we obtain that the asymptotic variance is equal to

H1 U 11 H1T

−1

H1 U 11 H1T

H1 U 11 H1T

−1

=

H1 U 11 H1T

−1 .

Consequently, a consistent estimator for the asymptotic variance of βˆnopt is given by −1 11 ˆ T ˆ ˆ ˆ ˆ ˆ H1 (βnopt )U (βnopt )H1 (βnopt ) , (12.42) ˆ 1 (β) and U ˆ 11 (β) were deﬁned by (12.24) and (12.28). where H Remark 4. The method we proposed for estimating the parameter β using restricted optimal (class 1) estimators only needs that the model for the coarsening probabilities be correctly speciﬁed. The appeal of this method is its simplicity. We did not have to use adaptive methods, where a simpler model p∗Z (z, ξ) had to be posited and an estimator for ξ had to be derived. Yet the resulting estimator is guaranteed to have the smallest asymptotic variance within the class of estimators considered. It would certainly be more eﬃcient than the simple inverse probability weighted complete-case estimator (IPWCC), which discards information from data that are not completely observed. However, this estimator is not eﬃcient. How close the variance of such an estimator will be to the semiparametric eﬃciency bound will depend F on how close the optimal element Beﬀ (Z) ∈ ΛF ⊥ is to the subspace G F and I(C=∞)B F (Z)

eff |Λ2 ], is to G2 . how close the optimal element in Λ2 , Π[ (∞,Z) If the missingness were by design, then restricted optimal (class 1) estimators would be guaranteed (subject to regularity conditions) to yield consistent, asymptotically normal estimators for β. However, if the coarsening probabilities are modeled and not correctly speciﬁed, then such estimators will be biased. There is no double-robustness protection guaranteed for such estimators. Therefore, in the next section, we consider what we refer to as (class 2) estimators. These estimators, although more complicated, will result in double-robust estimators that avoid the necessity to solve complicated integral equations.

Before discussing restricted optimal (class 2) estimators, we ﬁrst illustrate how to derive a restricted optimal (class 1) estimator by considering a speciﬁc example.

12.3 Example of an Optimal Restricted (Class 1) Estimator

309

12.3 Example of an Optimal Restricted (Class 1) Estimator We return to the example introduced in Section 7.4 where the goal is to estimate the parameter β in the restricted moment model E(Y |X) = µ(X, β). In this example, we let Y be a univariate response variable and X = (X (1) , X (2) )T be two univariate covariates. The second covariate X (2) is expensive to measure and therefore, by design, was only collected on a subsample of the n individuals in the study, whereas X (1) was collected on all n individuals. The subsample of individuals for which X (2) was collected was chosen at random with a prespeciﬁed probability that depended on Y and X (1) . This is an example of two levels of missingness, where we denote the complete-case indicator for the i-th individual as Ri (unscripted); that is, if Ri = 1, then we (1) (2) (1) observe (Y1 , Xi , Xi ), whereas if Ri = 0, then we observe (Yi , Xi ). The probability of a complete case is denoted by (1)

(1)

P (Ri = 1|Yi , Xi ) = P (Ri = 1|Yi , Xi ) = π(Yi , Xi ), (1)

(1)

where π(Yi , Xi ) (unscripted) is a known function of Yi and Xi . Since the coarsening of the data was by design, the corresponding coarsening tangent space Λψ = 0. Therefore, we need not worry that the vector J2 that spans G2 contains the score vector Sψ for this example. To deﬁne the restricted class of estimators, we must ﬁrst choose ﬁnitedimensional subsets G F ⊂ ΛF ⊥ ⊂ HF and G2 ⊂ Λ2 ⊂ H. We remind the reader that the space ΛF ⊥ consists of elements h(X, β0 )q×1 {Y −µ(X, β0 )} for arbitrary q-dimensional functions h(·) of X and the space Λ2 consists of elements f (Y, X (1) )q×1 {R − π(Y, X (1) )} for arbitrary q-dimensional functions of (Y, X

(1)

) .

Recall that X, by itself, refers to (X (1) , X (2) ). To deﬁne G F , we must choose a t1 -dimensional function of the full-data (Y, X), say J F (Y, X, β) = {J1F (Y, X, β), . . . , JtF1 (Y, X, β)}T , that spans G F , (1) (1) where the elements JjF (Y, X, β0 ) ∈ ΛF ⊥ , j = 1, . . . , t1 , and where ΛF ⊥ (1)

denotes the linear subspace in HF spanned by the ﬁrst element of the q(1) dimensional vector that makes up ΛF ⊥ ; i.e., ΛF ⊥ = {ΛF ⊥ }q .

310

12 Approximate Methods for Gaining Eﬃciency

Suppose, for example, we were considering a log-linear model, where µ(X, β) = exp(β1 + β2 X (1) + β3 X (2) ). If we also believed the variance as a function of X was homoscedastic, then the optimal full-data estimating function would be chosen to be m(Y, X, β) = DT (X, β)V −1 (X){Y − exp(β1 + β2 X (1) + β3 X (2) )}, where D(X, β) = ∂µ(X, β)/∂β T . Therefore, for this example, we would choose m(Y, X, β) = (1, X (1) , X (2) )T exp(β T X ∗ ){Y − exp(β T X ∗ )}, where X ∗ = (1, X (1) , X (2) )T and β = (β1 , β2 , β3 )T , as our optimal full-data estimating function if the data were not coarsened. However, this choice for m(Y, X, β) may not be optimal with coarsened data. Therefore, we consider restricted optimal estimators, where we might choose J F (Y, X) = f F (X) exp(β0T X ∗ ){Y − exp(β0T X ∗ )}, 2

2

where f F (X) = (1, X (1) , X (2) , X (1) , X (2) , X (1) X (2) )T . Such a set of basis functions allows for a quadratic relationship in X (1) and X (2) . We also deﬁne the t1 -dimensional vector of estimating functions as m∗ (Y, X, β) = f F (X) exp(β T X ∗ ){Y − exp(β T X ∗ )}.

(12.43)

To deﬁne G2 , we must choose a t2 -dimensional function of the observed data (R, Y, X (1) , RX (2) ), say J2 (·) = {J21 (·), . . . , J2t2 (·)}T , that spans G2 , (1) (1) where the elements J2j (·) ∈ Λ2 , j = 1, . . . , t2 , and where Λ2 denotes the linear subspace in H(1) spanned by the ﬁrst element of the q-dimensional vector that makes up Λ2 . For example, we might choose J2 (·) = f2 (Y, X (1) ){R − π(Y, X (1) )},

(12.44)

2

where f2 (Y, X (1) ) = (1, Y, X (1) , Y 2 , X (1) , Y X (1) )T . Such a set of basis functions allows for a quadratic relationship in Y and X (1) . Therefore, the class of restricted estimators that will be considered are solutions to the estimating equations (12.3), namely n Ri AF f F (Xi ) exp(β T X ∗ ){Yi − exp(β T X ∗ )} i

i=1

i

(1) π(Yi , Xi )

(1) (1) + {Ri − π(Yi , Xi )}A2 f2 (Yi , Xi ) = 0,

(12.45)

for an arbitrary q × t1 constant matrix AF and an arbitrary q × t2 constant matrix A2 . For this illustration, q = 3 and t1 = t2 = 6.

12.3 Example of an Optimal Restricted (Class 1) Estimator

311

Finding the optimal estimator within this restricted class and deriving the asymptotic variance are now just a matter of plugging into the formulas derived in the previous section. Taking the partial derivative of (12.43) with respect to β yields ∂m∗ (Y, X, β0 ) = −f F (X) exp(β0T X ∗ )D(X, β0 ) E ∂β T T

= f F (X) exp(2β0T X ∗ )X ∗ . Therefore, we obtain a consistent estimator for H1 (β) by using ˆ 1 (β) = n−1 H

n

Ri

T

(1) i=1 π(Yi , Xi )

Xi∗ exp(2β T Xi∗ )f F (Xi ).

(12.46)

Next we use equations (12.25)–(12.27) to compute ˆ11 (β) = n−1 U

n Ri {Yi − exp(β T X ∗ )}2 i

(1) π 2 (Yi , Xi )

i=1

ˆ12 (β) = n−1 U

n (1) Ri {Ri − π(Yi , X )} i

i=1

(1) π(Yi , Xi )

T

f F (Xi )f F (Xi ),

(12.47)

(1)

{Yi −exp(β T Xi∗ )}f F (Xi )f2T (Yi , Xi ), (12.48)

and ˆ22 = n−1 U

n

(1)

(1)

(1)

{Ri − π(Yi , Xi )}2 f2 (Yi , Xi )f2T (Yi , Xi ).

(12.49)

i=1

From these, we can compute ˆ11 (β) − U ˆ12 (β)U ˆ −1 (β)U ˆ T (β)}−1 , ˆ 11 (β) = {U U 12 22 ˆ 12 (β) = −U ˆ 11 (β)U ˆ12 (β)U ˆ −1 (β), U 22

(12.50) (12.51)

ˆ ˆ 11 AˆF opt (β) = H1 (β)U (β),

(12.52)

ˆ 1 (β)U ˆ 12 (β). Aˆ2opt (β) = H

(12.53)

and

Therefore, the optimal restricted estimator is the solution to the estimating equation n F T ∗ T ∗ Ri AˆF opt (β)f (Xi ) exp(β Xi ){Yi − exp(β Xi )} (1)

π(Yi , Xi )

i=1

+ {Ri −

(1) (1) π(Yi , Xi )}Aˆ2opt (β)f2 (Yi , Xi )

= 0,

(12.54)

312

12 Approximate Methods for Gaining Eﬃciency

which we denote by βˆnopt . Moreover, the asymptotic variance for βˆnopt can be estimated using −1 11 ˆ T ˆ ˆ ˆ ˆ ˆ H1 (βnopt )U (βnopt )H1 (βnopt ) , ˆ 11 (β) were deﬁned by (12.46) and (12.50), respectively. where Hˆ1 (β) and U

Modeling the Missingness Probabilities In the example above, it was assumed that the missing values of X (2) were by design, where the investigator had control of the missingness probabilities. We now consider how the methods would be modiﬁed if these probabilities were not known and had to be estimated from the data. We might, for instance, consider the logistic regression model where P (R = 1|Y, X (1) ) = π(Y, X (1) , ψ) =

exp(ψ0 + ψ1 Y + ψ2 X (1) ) . (12.55) 1 + exp(ψ0 + ψ1 Y + ψ2 X (1) )

The maximum likelihood estimator for ψ = (ψ0 , ψ1 , ψ2 )T is obtained by maximizing the likelihood given by (8.10); speciﬁcally, $ # n (1) ! exp{(ψ0 + ψ1 Yi + ψ2 Xi )Ri } , (1) 1 + exp(ψ0 + ψ1 Yi + ψ2 Xi ) i=1 and the resulting MLE is denoted by ψˆn = (ψˆ0n , ψˆ1n ψˆ2n )T . Using standard results for the logistic regression model, we note that the score vector Sψ (·) is given by Sψ (R, Y, X (1) , ψ) = (1, Y, X (1) )T {R − π(Y, X (1) , ψ)},

(12.56)

where π(Y, X (1) , ψ) is the probability of a complete case deﬁned in (12.55). We also note that the score equation, evaluated at the MLE, is equal to zero; that is, n (1) Sψ (Ri , Yi , Xi , ψˆn ) = 0. (12.57) i=1

As mentioned in Remark 3 of this chapter, we should choose the score vector Sψ (·) as part of a set of basis functions J2 (·) that spans the space G2 . Examining the set of functions J2 (·) given by (12.44) for the example above, we note that indeed Sψ (·) makes up the ﬁrst three elements of J2 (·). The estimator for β would then be identical to that given in equation (1) (12.54) except that we would substitute π(Yi , Xi , ψˆn ) for π(Y, X (1) ) in the equation (12.54) itself and when evaluating all the quantities in equations (12.46)–(12.53).

12.4 Optimal Restricted (Class 2) Estimators

313

Remark 5. We point out that the second term in the estimating equation (12.54) (i.e., the augmented term) can be written as Aˆ2opt

n

(1)

(1)

f2 (Yi , Xi ){Ri − π(Yi , Xi , ψˆn )}.

i=1

Since the ﬁrst three elements of the vector n

(1) (1) f2 (Yi , Xi ){Ri − π(Yi , Xi , ψˆn )}

(12.58)

i=1

n (1) are i=1 Sψ (Ri , Yi , Xi , ψˆn ), then as a consequence of (12.57), this means that the ﬁrst three elements of the vector (12.58) are equal to zero. This observation may result in some modest savings in computation.

12.4 Optimal Restricted (Class 2) Estimators (Class 2) restricted estimators are AIPWCC estimators where we restrict attention to a ﬁnite-dimensional linear subspace G F ∈ ΛF ⊥ spanned by the vector J F (Z), as we did for (class 1) restricted estimators, but where G2 = Λ2 . By so doing, we will show that the resulting optimal estimator for β within this class will have an inﬂuence function that is an element of the class of doublerobust inﬂuence functions (IF )DR ; see Deﬁnition 2 of Chapter 10. Toward that end, we deﬁne the linear space Ξ similar to what we did in (12.4); that is, I(C = ∞)G F (12.59) Ξ= ⊕ Λ2 , (∞, Z) and Ξ consists of the elements I(C = ∞)AF m∗ (Z, β0 ) + L2 {C, GC (Z)} (∞, Z)

(12.60)

q×t1

and L2 ∈ Λ2 , where m∗ (Z, β) is a t1 × 1 in H for any constant matrix AF vector of estimating functions such that m∗ (Z, β0 ) = J F (Z). We denote an element within this class as ϕ∗ {C, GC (Z)} and deﬁne IF (Ξ) to be the elements within this class that satisfy (12.7). The elements within the class IF (Ξ) are deﬁned as ϕ{C, GC (Z)} and are given by (12.8). We now prove the following key theorem for (class 2) restricted estimators. Theorem 12.3. Among the elements ϕ{C, GC (Z)} ∈ IF (Ξ), the one with the smallest variance matrix is given by ϕopt {C, GC (Z)} = J {ϕF opt (Z)},

(12.61)

314

12 Approximate Methods for Gaining Eﬃciency

where the linear operator J (·) is given by Deﬁnition 1 of Chapter 10; that is, $ # I(C = ∞)ϕF I(C = ∞)ϕF opt (Z) opt (Z) F Λ2 , J {ϕopt (Z)} = −Π (∞, Z) (∞, Z) T

∗F F −1 ∗F ϕF ϕopt (Z), opt (Z) = [E{ϕopt (Z)Sβ (Z)}]

and

ϕ∗F opt (Z)

is the unique element in G

F

(12.62)

that solves the equation

F Π[SβF (Z) − M−1 {ϕ∗F opt (Z)}|G ] = 0,

(12.63)

where M−1 is the inverse of the linear operator M given by Deﬁnition 5 of Chapter 10. (The inverse operator M−1 exists and is unique; see Lemma 10.5.) Proof. We ﬁrst will prove that IF (Ξ) consists of the class of elements I(C = ∞)ϕF (Z) + L∗2 {C, GC (Z)}, (∞, Z) where

(12.64)

T

ϕF (Z) = [E{ϕ∗F (Z)SβF (Z)}]−1 ϕ∗F (Z),

ϕ∗F (Z) ∈ G F , and L∗2 {C, GC (Z)} ∈ Λ2 . Because the elements in IF (Ξ) are deﬁned by (12.8), (12.64) will be true if we can show that T

E[ϕ∗ {C, GC (Z)}SβT {C, GC (Z)}] = E{ϕ∗F (Z)SβF (Z)},

(12.65)

where ϕ∗ {C, GC (Z)} ∈ Ξ is equal to ϕ∗ {C, GC (Z)} =

I(C = ∞)ϕ∗F (Z) + L2 {C, GC (Z)}. (∞, Z)

Because Sβ {C, GC (Z)} = E{SβF (Z)|C, GC (Z)}, we use a series of iterated conditional expectations to obtain T

E[ϕ∗ {C, GC (Z)}SβT {C, GC (Z)}] = E(E[ϕ∗ {C, GC (Z)}SβF (Z)|C, GC (Z)]) T

= E[ϕ∗ {C, GC (Z)}SβF (Z)] T

= E(E[ϕ∗ {C, GC (Z)}SβF (Z)|Z]) T

= E(E[ϕ∗ {C, GC (Z)}|Z]SβF (Z)) T

= E{ϕ∗F (Z)SβF (Z)}, thus proving (12.65). By proving (12.64), we have demonstrated that all the elements in IF (Ξ) can be written as

12.4 Optimal Restricted (Class 2) Estimators

ϕ{C, GC (Z)} =

I(C = ∞)ϕF (Z) + L2 {C, GC (Z)}, (∞, Z)

315

(12.66)

where ϕF (Z) is a full-data inﬂuence function and L2 ∈ Λ2 . Because of Theorem 10.1, we know that, for a ﬁxed ϕF (Z), the optimal element (smallest variance matrix) among the class of elements (12.66) is given by J {ϕF (Z)}. Therefore, we only need to restrict attention to those elements of IF (Ξ) that are in the class F F ∗F FT −1 ∗F J {ϕ (Z)} : ϕ (Z) = [E{ϕ (Z)Sβ (Z)}] ϕ (Z) , where ϕ∗F (Z) ∈ G F , if the goal is to ﬁnd the optimal estimator in IF (Ξ). Equivalently, we can restrict the search to the elements in the linear subspace J (G F ) ⊂ Ξ that satisfy (12.7). In Theorem 10.6, we proved that J {hF (Z)} = L[M−1 {hF (Z)}], where the linear operator L was deﬁned by Deﬁnition 4 of Chapter 10 and, we remind the reader, is given by L{hF (Z)} = E{hF (Z)|C, GC (Z)}. Therefore, the linear space J (G F ) = L{M−1 (G F )}. Using the same proof as for Theorem 12.1, we can also prove that among the elements ϕ{C, GC (Z)} ∈ J (G F ) = L{M−1 (G F )}, the one with the smallest variance matrix is the normalized version of the projection of the observeddata score vector onto J (G F ). Speciﬁcally, ϕopt {C, GC (Z)} =

−1 E[ϕ∗opt {C, GC (Z)}SβT {C, GC (Z)}] ϕ∗opt {C, GC (Z)}, (12.67)

where ϕ∗opt {C, GC (Z)} = Π[Sβ {C, GC (Z)}|L{M−1 (G F )}].

(12.68)

We now show how to derive this projection. Because of the projection theorem for Hilbert spaces, the projection of Sβ {C, GC (Z)} onto the closed linear space L{M−1 (G F )} is the unique element F ϕ∗F that satisﬁes opt (Z) ∈ G E

−1

Sβ {C, GC (Z)} − L[M

T

{ϕ∗F opt (Z)}]

−1

L[M

{ϕ

∗F

(Z)}] = 0, (12.69)

for all ϕ∗F (Z) ∈ G F . Recalling that Sβ {C, GC (Z)} = E{SβF (Z)|C, GC (Z)} and L[M−1 {ϕ∗F (Z)}] = E{M−1 (ϕ∗F )|C, GC (Z)}, we use a series of iterated conditional expectations to write (12.69) as

316

12 Approximate Methods for Gaining Eﬃciency

T −1 ∗F 0 = E E[{SβF − M−1 (ϕ∗F )} L{M (ϕ )}|C, G (Z)] C opt T −1 ∗F = E {SβF − M−1 (ϕ∗F )} L{M (ϕ )} opt T −1 ∗F = E E[{SβF − M−1 (ϕ∗F )} L{M (ϕ )}|Z] opt F −1 ∗F T −1 ∗F = E {Sβ − M (ϕopt )} E[L{M (ϕ )}|Z] F −1 ∗F T −1 ∗F = E {Sβ − M (ϕopt )} M{M (ϕ )} F −1 ∗F T ∗F = E {Sβ − M (ϕopt )} ϕ .

(12.70)

Therefore, (12.70) being true for all ϕ∗F ∈ G F implies that SβF − M−1 (ϕ∗F opt ) must be orthogonal to G F . Since the projection exists and is unique, this F implies that there must exist a unique element ϕ∗F such that (12.70) opt ∈ G ∗F F holds for all ϕ ∈ G , or equivalently F Π[SβF (Z) − M−1 {ϕ∗F opt (Z)}|G ] = 0.

(12.71)

Consequently, ∗F ϕ∗opt {C, GC (Z)} = L[M−1 {ϕ∗F opt (Z)}] = J {ϕopt (Z)},

where ϕ∗F opt (Z) satisﬁes (12.71), or (12.63) of the theorem. The proof is complete if we can show that E[ϕ∗opt {C, GC (Z)}SβT {C, GC (Z)}] of equation (12.67), where ϕ∗opt {C, GC (Z)} = L[M−1 {ϕ∗F opt (Z)}], T

F is the same as E{ϕ∗F opt (Z)Sβ (Z)}. This can be shown by using the same iterated expectations argument that led to (12.70).

A corollary that is an immediate consequence of Theorem 12.3 by taking G F = ΛF ⊥ is given as follows. Corollary 2. Among all inﬂuence functions in I(C = ∞)ΛF ⊥ Ξ= ⊕ Λ2 (∞, Z) (that is, elements of Ξ that satisfy (12.7)), the one with the smallest variance matrix is obtained by choosing

12.4 Optimal Restricted (Class 2) Estimators

317

ϕ∗opt {C, GC (Z)} = J {ϕ∗F opt (Z)}, F⊥ where ϕ∗F that satisﬁes opt (Z) is the unique element in Λ F⊥ Π[SβF (Z) − M−1 {ϕ∗F ]=0 opt (Z)}|Λ

or equivalently solves the equation F⊥ F Π[M−1 {ϕ∗F ] = Π[SβF (Z)|ΛF ⊥ ] = Seﬀ (Z). opt (Z)}|Λ

Remark 6. Since the space Ξ in the corollary is the same as Λ⊥ η , then the result above is an alternative proof of Theorem 11.1, which was used to derive the optimal inﬂuence function among all AIPWCC estimators for β. Returning to the restricted (class 2) estimators, we obtain the following corollary. Corollary 3. Let m∗ (Z, β) be a t1 × 1 (t1 > q) vector of estimating functions such that m∗ (Z, β0 ) = J F (Z) spans the linear space G F ⊂ ΛF ⊥ . Then, among the class of inﬂuence functions IF (Ξ), where Ξ is deﬁned by (12.59), the optimal element (smallest variance matrix) is given by ϕF opt (Z), deﬁned by (12.62), the normalized version of q×t1

F ϕ∗F opt = Aopt

m∗ (Z, β0 ),

where q×t1

AF opt

T −1 ∂m∗ (Z, β0 ) −1 ∗ ∗T E[M =− E {m (Z, β )}m (Z, β )] . 0 0 ∂β T (12.72) q×t1

F Proof. Using (12.63), we are looking for ϕ∗F m∗ (Z, β0 ) such that opt = Aopt q×t1 ∗ F Π SβF (Z) − M−1 {AF = 0. (12.73) m (Z, β )} G 0 opt

Because G F is spanned by m∗ (Z, β0 ), the standard results for projecting onto a ﬁnite-dimensional linear space yield T

T

Π[hF (Z)|G F ] = E(hF m∗ ){E(m∗ m∗ )}−1 m∗ (Z, β0 ).

(12.74)

Using (12.74), we write equation (12.73) as T

T

−1 E[{SβF − AF (m∗ )}m∗ ]{E(m∗ m∗ )}−1 m∗ (Z, β0 ) = 0. opt M

(12.75)

Because m∗ (Z, β0 ) is made up of t1 linearly independent elements, this implies T that the variance matrix E(m∗ m∗ ) is positive deﬁnite and hence has a unique inverse. Consequently, equation (12.75) is true if and only if

318

12 Approximate Methods for Gaining Eﬃciency T

−1 E[{SβF − AF (m∗ )}m∗ ] = 0 opt M

or when

T

T

−1 AF (m∗ )m∗ } = E(SβF m∗ ). opt E{M

(12.76)

Using the result from equation (12.12) of Lemma 12.1, we obtain that T E{SβF (Z)m∗ (Z, β0 )}

T ∂m∗ (Z, β0 ) =− E . ∂β T

Substituting this last result into equation (12.76) and solving for AF opt leads to (12.72), thus proving the corollary. The results of Corollary 3 are especially useful when the inverse operator M−1 (·) can be derived explicitly such as the case when there are two levels of coarsening or when the coarsening is monotone. The following algorithm can now be used to derive improved adaptive double-robust estimators for β: 1. If the coarsening of the data is not by design, we develop a model for the coarsening probabilities, say P (C = r|Z) = {r, Gr (Z), ψ}, and estimate ψ by maximizing n !

{Ci , GCi (Zi ), ψ}.

i=1

We denote this estimator by ψˆn . 2. We posit a simpler parametric model for the distribution of Z using p∗Z (z, ξ) and estimate ξ by maximizing the likelihood n ! i=1

p∗Gr

i

(Zi ) (gri , ξ)

for a realization of the data (ri , gri ), i = 1, . . . , n, where p∗Gr (Z) (gr , ξ) = p∗Z (z, ξ)dνZ (z). {z:Gr (z)=gr }

We denote the estimator by ξˆn∗ . 3. We consider a t1 ×1 vector m∗ (Z, β) of estimating functions, where t1 > q. These may include the q-dimensional eﬃcient full-data estimating function as a subset of m∗ (Z, β).

12.4 Optimal Restricted (Class 2) Estimators

319

ˆ ˆ∗ 4. We compute AF opt (β, ψn , ξn ) by using AF opt (β, , ψ, ξ)

T ∂m∗ (Z, β) =− E ,ξ ∂β T −1 T × E[M−1 {m∗ (Z, β), ψ, ξ}m∗ (Z, β), ξ] ,

where we emphasize that M−1 is computed as a function of ψ and ξ and expectations of functions of Z are computed as a function of ξ. 5. We compute I(C = ∞)m∗ (Z, β) L2t1 ×1 {C, GC (Z), β, ψ, ξ} = −Π Λ2 , ψ, ξ . (∞, Z, ψ) 6. The improved double-robust estimator for β is given as the solution to the AIPWCC estimating equation n

ˆ ˆ∗ AF opt (β, ψn , ξn )

i=1

I(Ci = ∞)m∗ (Zi , β) (∞, Zi , ψˆn )

+ L2 {Ci , GCi (Zi ), β, ψˆn , ξˆn∗ } = 0.

(12.77)

Since this is an AIPWCC estimator, the asymptotic variance can be estimated using the sandwich variance estimator given by (9.19). Logistic Regression Example Revisited In Section 10.2, we developed a double-robust estimator for the parameters in a logistic regression model when one of the covariates was missing for some individuals. Speciﬁcally, we considered the model P (Y = 1|X) =

exp(β T X ∗ ) , 1 + exp(β T X ∗ )

where X = (X1T , X2 )T , X ∗ = (1, X1T , X2 )T , (Y, X1 ) were always observed, whereas the covariate X2 may be missing for some of the individuals in a study. A complete-case indicator was denoted by R, and it was assumed that P (R = 1|Y, X) = π(Y, X1 , ψ) =

exp(ψ0 + ψ1 Y + ψ2T X1 ) . 1 + exp(ψ0 + ψ1 Y + ψ2T X1 )

The estimator ψˆn is obtained by maximizing the likelihood n ! exp{(ψ0 + ψ1 Yi + ψ T X1i )Ri } 2

i=1

1 + exp(ψ0 + ψ1 Yi + ψ2T X1i )

.

320

12 Approximate Methods for Gaining Eﬃciency

We also posited a model for the full data (Y, X) by assuming that the conditional distribution of X given Y follows a multivariate normal distribution with a mean that depends on Y but with a variance matrix that is independent of Y . Let us denote the mean vector of X given Y = 1 and Y = 0 as µ1 and µ0 , respectively, and the common covariance matrix as Σ. We also denote the mean vector of X1 given Y = 1 and Y = 0 as µ11 and µ10 , respectively, and the mean of X2 given Y = 1 and Y = 0 as µ21 and µ20 , respectively. Similarly, we denote the variance matrix of X1 by Σ11 , the variance of the single covariate X2 by Σ22 , and the covariance of X1 and X2 by Σ12 . The parameter ξ for this posited model can be represented by ξ = (µ1 , µ0 , Σ, τ ), where τ denotes P (Y = 1). Since Y is observed for everyone, the estimate for τ is obtained by the sample proportion τˆn = n−1

n

Yi .

i=1

The estimates for µ1 , µ0 , and Σ are obtained by maximizing the observed-data likelihood (1−Ri )I(Yi =k) n ! 1 ! 1 −1/2 T −1 |Σ11 | exp − (X1i − µ1k ) Σ (X1i − µ1k ) 2 i=1 k=0 Ri I(Yi =k) 1 . ×|Σ|−1/2 exp − (Xi − µk )T Σ−1 (Xi − µk ) 2 With full data we know that the optimal estimating function is given by exp(β T X ∗ ) ∗ . m(Y, X, β) = X Y − 1 + exp(β T X ∗ ) Since this may no longer be the optimal choice with coarsened data, we now consider an expanded set of estimating functions, namely m∗ (Y, X, β). For example, we might take exp(β T X ∗ ) ∗ ∗∗ , Y − m (Y, X, β) = X 1 + exp(β T X ∗ ) where X ∗∗ is a vector consisting of X ∗ together with all the squared terms and cross-product terms of X. To ﬁnd the optimal estimator using m∗ (Y, X, β) deﬁned above, we must compute AF opt (β, ψ, ξ) in equation (12.72), as described in step 4 of the algorithm. Toward that end, we ﬁrst note that −

T exp(β T X ∗ ) ∂m∗ (Y, X, β) = X ∗∗ X∗ . T T ∗ 2 ∂β {1 + exp(β X )}

Also, with two levels of missingness,

12.5 Recap and Review of Notation

321

M−1 {m∗ (Y, X, β), ψ, ξ} = {π(Y, X1 , ψ)}−1 m∗ (Y, X, β) −

1 − π(Y, X1 , ψ) E{m∗ (Y, X, β)|Y, X1 , ξ}. π(Y, X1 , ψ)

Finally, with two levels of missingness, we use the results from Theorem 10.2 to obtain I(C = ∞)m∗ (Z, β) Λ2 , ψ, ξ L2 {C, GC (Z), β, ψ, ξ} = − Π (∞, Z, ψ) R − π(Y, X1 , ψ) E{m∗ (Y, X, β)|Y, X1 , ξ}. =− π(Y, X1 , ψ) Remark 7. Because Y is a binary indicator and the distribution of X given Y is multivariate normal, it would be easy to simulate full data (Y, X) from such a joint distribution. Such simulated can then be used to estimate un data ∂m∗ (Y,X,β) , ξ , which for this example conditional expectations such as E − ∂β T is exp(β T X ∗ ) ∗T E X ∗∗ X , ξ , {1 + exp(β T X ∗ )}2 using Monte Carlo methods. Similarly, because the conditional distribution of X2 given X1 , Y is normally distributed with mean µ21 Y + µ20 (1 − Y ) + Σ12 Σ−1 22 {X1 − µ11 Y − µ10 (1 − Y )} T and variance Σ12 Σ−1 22 Σ12 , Monte Carlo methods can be used when computing conditional expectations of functions of (Y, X) given (Y, X1 ), as was necessary to compute M−1 or L2 (·).

We are ﬁnally in a position to deﬁne all the elements making up equation (12.77), which we can use to derive the estimator for β.

12.5 Recap and Review of Notation •

In this chapter, we considered a restricted class of AIPWCC estimators where observed-data estimating functions were chosen from the linear sub⊥ space Π[Ξ|Λ⊥ ψ ] ⊂ Λ , where Ξ=

I(C = ∞)G F ⊕ G2 , (∞, Z)

where G F was a linear subspace contained in ΛF ⊥ , and G2 was a linear subspace contained in the augmentation space Λ2 . • We considered two classes of restricted estimators:

322

12 Approximate Methods for Gaining Eﬃciency

(Class 1) were deﬁned by letting both G F ⊂ ΛF ⊥ and G2 ⊂ Λ2 be ﬁnitet1 ×1 dimensional linear spaces spanned by J F (Z) and J2t2 ×1 {C, GC (Z)}, respectively, where t1 > q and where the elements of the coarsening model score vector Sψ {C, GC (Z)} were elements contained in J2t2 ×1 {C, GC (Z)}. – (Class 2) were deﬁned by only letting G F ⊂ ΛF ⊥ be a ﬁnite-dimensional t1 ×1 linear space spanned by J F (Z), with t1 > q, but took G2 = Λ2 . • The optimal inﬂuence functions within the class Ξ were derived for both classes and shown to be orthogonal to Λψ . This then allowed us to derive optimal restricted AIPWCC estimators within these two classes. – The (class 1) restricted optimal estimators were the easiest to compute but not double robust. – The (class 2) restricted optimal estimators resulted in double-robust estimators. They were, however, computationally more intensive but not as diﬃcult to compute as the locally eﬃcient estimators of Chapter 11. –

12.6 Exercises for Chapter 12 1. In Section 11.2, we outlined the steps that would be necessary to obtain a locally eﬃcient estimator for β for the restricted moment model E(Y |X) = µ(X, β) when the data are monotonically coarsened. This methodology led to the integral equation (11.42), which in general is very diﬃcult if not impossible to solve. Only consider the case when Y is a univariate random variable. For this same problem, outline the steps that would be necessary to obtain the optimal restricted (class 2) estimator for β. For this exercise, take m∗ (Y, X, β) = f t1 ×1 (X, β){Y − µ(X, β)}, where f t1 ×1 (X, β) is a t1 × 1 vector of linearly independent functions of X and β and t1 > q. Assume that you can estimate the parameter ψ in the coarsening model and the parameter ξ in the posited model p∗Z (z, ξ).

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

Statistical inference generally focuses on the associational relationships between variables in a population. Data that are collected are assumed to be realizations of iid random vectors Z1 , . . . , Zn where a single observation Z is distributed according to some density in the model pZ (z, θ), where θ denotes parameters that describe important features of the relationships of the variables of interest. However, one may be interested in causal relationships. That is, does “A” cause “B”? For example, does a treatment intervention or exposure at one point in time have a causal eﬀect on subsequent response? In order to formulate such a question from a statistical perspective, we will take the point of view advocated by Neyman (1923), Rubin (1974), Robins (1986), and Holland (1986), who considered potential outcomes. We will illustrate that the semiparametric theory developed in this book can be used to aid us in ﬁnding eﬃcient semiparametric estimators of the average causal treatment eﬀect under certain assumptions. The methods we will discuss only consider the simplest case of point exposure; that is, exposure or treatment of individuals at only one point in time. A much more complex and elegant theory has been developed by Robins and colleagues that provides methods for studying the eﬀect of time-dependent treatments on response. We refer the reader to the book by van der Laan and Robins (2003) for more details and references.

13.1 Point Exposure Studies We shall denote the possible treatments or exposures that can be given or experienced by an individual by the random variable A. For simplicity, we will assume that there are two possible treatments that we wish to compare and thus A will be a binary variable taking on the values 0 or 1. For example, A may denote whether an individual with hypertension is treated with a statin drug (A = 1) or not (A = 0). The response variable will be denoted by Y , say, change in blood pressure after three months. Hence, in a typical study of

324

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

this treatment, we consider a population of patients with hypertension, say individuals whose diastolic blood pressure is greater than or equal to 140, and identify a sample of such patients, some of which receive the statin drug (A = 1) and others who do not (A = 0). This sample of individuals is followed for three months, and the change in blood pressure Y is measured. Ultimately, we are interested in establishing a causal link between treatment and response. That is, does treatment with the statin drug reduce blood pressure after three months as compared with no treatment? The data that are available from such a study may be summarized by Zi = (Yi , Ai , Xi ), i = 1, . . . , n, where for the i-th individual Yi denotes the response, Ai the treatment received, and Xi other covariates that have been measured prior to treatment (i.e., baseline covariates). In a typical associational analysis, we might deﬁne population parameters µ1 = E(Y |A = 1), µ0 = E(Y |A = 0), and ∆ = µ1 − µ0 . That is, ∆ denotes the diﬀerence in mean response for individuals receiving treatment 1 and the mean response for individuals receiving treatment 0. Without any additional assumptions, we can estimate ∆ simply as the diﬀerence of the treatmentˆ =µ speciﬁc sample average of response, namely ∆ ˆ1 − µ ˆ0 , where µ ˆ1 = n−1 1

n i=1

Ai Yi ,

µ ˆ0 = n−1 0

n

(1 − Ai )Yi ,

i=1

and n1 = Ai , n0 = (1 − Ai ) denote the treatment-speciﬁc sample sizes. Typically, such an associational analysis does not answer the causal question of interest. If the treatments were not assigned to the patients at random, then one can easily imagine that individuals who receive the statin drugs may be inherently diﬀerent from those who do not. They may be wealthier, younger, smoke less, etc. Consequently, the associational parameter ∆ = µ1 − µ0 may reﬂect these inherent diﬀerences as well as any eﬀect due to treatment. In the study of epidemiology, such factors are referred to as confounders, as they may confound the relationship between treatment and response. Thus, we have argued that statistical associations may not be adequate to describe causal eﬀects. Therefore, how might we describe causal eﬀects? The point of view we will adopt is that proposed by Neyman (1923) and Rubin (1974), where causal eﬀects are deﬁned through potential outcomes or counterfactual random variables. Speciﬁcally, for each level of the treatment A = a, we will assume that there exists a potential outcome Y ∗ (a), where Y ∗ (a) denotes the response of a randomly selected individual had that individual, possibly contrary to fact, been given treatment A = a. In our illustration, we only include two treatments and hence we deﬁne the potential outcomes Y ∗ (1) and Y ∗ (0). Again, we emphasize that these are referred to as potential outcomes or counterfactual random variables, as it is impossible to observe both Y ∗ (1) and Y ∗ (0) simultaneously. Nonetheless, using the notion of potential outcomes, we would deﬁne the causal treatment eﬀect by Y ∗ (1) − Y ∗ (0).

13.1 Point Exposure Studies

325

This deﬁnition of causal treatment eﬀect is at the subject-speciﬁc level and, as we pointed out, is impossible to measure. However, it may be possible, under certain assumptions, to estimate the population-level causal treatment eﬀect, i.e., the expected value of the subject-speciﬁc treatment eﬀect denoted as δ = E{Y ∗ (1) − Y ∗ (0)} = E{Y ∗ (1)} − E{Y ∗ (0)}. The parameter δ is referred to as the average causal treatment eﬀect. Since the average causal treatment eﬀect is deﬁned from parameters describing the potential outcomes, which are not directly observable, the question is whether this parameter can be deduced from parameters describing the distribution of the observable random variables Z = (Y, A, X). Using the ideas that were developed for missing-data problems, we consider the full data to be the variables {Y ∗ (1), Y ∗ (0), A, X}, which involve the potential outcomes as well as the treatment assignment and baseline covariates, and the observed data to be (Y, A, X). We now make the reasonable assumption that Y = AY ∗ (1) + (1 − A)Y ∗ (0); (13.1) that is, the observed response Y is equal to Y ∗ (1) if the subject was given treatment A = 1 and is equal to Y ∗ (0) if the subject was given treatment A = 0. Remark 1. Rubin (1978a) refers to the assumption (13.1) as the Stable Unit Treatment Value Assumption, or SUTVA. Although this assumption may seem straightforward at ﬁrst, there are some philosophical subtleties that need to be considered in order to fully accept. For one thing, there must not be any interference in the response from other subjects. That is, the observed response for the i-th individual in the sample should not be aﬀected by the response of the other individuals in the sample. Thus, for example, this assumption may not be reasonable in a vaccine intervention trial for an infectious disease, where the response of an individual is clearly aﬀected by the response of others in the study. That is, whether or not an individual contracts an infectious disease will depend, to some extent, on whether and how many other individuals in the population are infected. From here on, we will assume the SUTVA assumption but caution that the plausibility of this assumption needs to be evaluated on a case-by-case basis. Because of assumption (13.1), we see that the observed data are a manyto-one transformation of the full data. We also note that the treatment assignment indicator A plays a role similar to that of the missingness indicator in missing-data problems. This analogy to missing-data problems will be useful as we develop the theory that enables us to estimate the average causal treatment eﬀect.

326

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

13.2 Randomization and Causality Intuitively, it has been accepted that the use of a randomized intervention study will result in an unbiased estimate of the average treatment eﬀect with causal interpretations. This is because patients are assigned to treatment interventions according to a random mechanism that is independent of all other factors. Therefore, individuals in the two treatment groups are similar, on average, with respect to all characteristics except for the treatment intervention to which they were assigned. Consequently, diﬀerences in response between the two randomized groups can be reasonably attributed to the eﬀect of treatment and not other extraneous factors. We now formalize this notion through the use of potential outcomes. Speciﬁcally, we will show that the observed treatment diﬀerence in a randomized intervention study is an unbiased estimator of the average causal treatment eﬀect δ. Together with the SUTVA assumption (13.1), we also make the assumption that A⊥ ⊥ {Y ∗ (1), Y ∗ (0)}, (13.2) where “⊥ ⊥” denotes independence. This assumption is plausible since the response of an individual to one treatment or the other should be independent of treatment assignment because treatment was assigned according to some random mechanism. Remark 2. The assumption that treatment assignment is independent of the potential outcomes should not be confused with treatment assignment being independent of the observed response. That is, A⊥ ⊥ {Y ∗ (1), Y ∗ (0)} does not imply that A ⊥ ⊥ Y = AY ∗ (1) + (1 − A)Y ∗ (0). Generally, we reserve the notion that A ⊥ ⊥ Y to denote the null hypothesis that there is no treatment eﬀect, whereas if there is a treatment eﬀect, then A is not independent of Y . In a randomized study, the associational treatment eﬀect ∆ = E(Y |A = 1) − E(Y |A = 0) is equal to the average causal treatment eﬀect δ = E{Y ∗ (1)} − E{Y ∗ (0)}, as we now demonstrate: E(Y |A = 1) = E{AY ∗ (1) + (1 − A)Y ∗ (0)|A = 1} = E{Y ∗ (1)|A = 1} (13.3) = E{Y ∗ (1)},

(13.4)

where (13.3) follows from the SUTVA assumption and (13.4) follows from assumption (13.2). Similarly, we can show that E(Y |A = 0) = E{Y ∗ (0)}. Consequently, in the sample’s average response between treatn the diﬀerence −1 n ments n−1 A Y − n 1 0 i=1 i i i=1 (1 − Ai )Yi , which is an unbiased estimator

13.3 Observational Studies

327

for ∆, the associational treatment eﬀect, is also an unbiased estimator for the average causal treatment eﬀect δ in a randomized intervention study. As we mentioned earlier, the treatment indicator A serves a role analogous to the missingness indicator R, which was used to denote missing data with two levels of missingness. That is, when A = 1, we observe Y ∗ (1), in which case Y ∗ (0) is missing, and when A = 0, we observe Y ∗ (0) and Y ∗ (1) is missing. The assumption (13.2), which is induced because of randomization, is similar to missing completely at random; that is, the probability that A is equal to 1 or 0 is independent of all the data {Y ∗ (1), Y ∗ (0), X}.

13.3 Observational Studies In an observational study, individuals are not assigned to treatment by an experimental design but rather by choice. It may be that data from an observational study are easier or cheaper to collect, or it may be that conducting a randomized study is infeasible or unethical. Clearly, if we want to evaluate the eﬀect of smoking on some health outcome, it would be unethical to randomize individuals and force them to smoke or not smoke. For simplicity, and without loss of generality, we again consider only two treatments. In an observational study, individuals who receive treatment A = 1 may not be prognostically comparable with those who receive treatment A = 0. Therefore, it may no longer be reasonable to assume that A⊥ ⊥ {Y ∗ (1), Y ∗ (0)}. However, if pretreatment (baseline) prognostic factors X can be identiﬁed that aﬀect treatment choice and, in addition, are themselves prognostic (i.e., related to clinical outcome), then it may be reasonable to assume A⊥ ⊥ {Y ∗ (1), Y ∗ (0)}|X; (13.5) that is, treatment assignment is independent of the potential outcomes given X. Such variables X are referred to in epidemiology as confounders, and assumption (13.5) is sometimes referred to as the assumption of “no unmeasured confounders.” Rubin (1978a) also refers to this assumption as the strong ignorability assumption. Remark 3. The assumption of no unmeasured confounders is key to being able to estimate the average causal treatment eﬀect in an observational study. Presumably, when a patient or his or her physician is faced with a binary treatment choice of whether to treat the patient with treatment A = 0 or A = 1, they do not know what the patient’s potential outcome will be. Consequently, treatment choice is made based on variables and characteristics of the patient prior to or at the time of treatment. If such factors are captured in the database, then the assumption of no unmeasured confounders may be reasonable. Of course, there may be factors that inﬂuence treatment decisions that are not captured in the data that are collected. If such factors are also correlated with response, then the assumption (13.5) is no longer tenable.

328

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

We now give an argument to show that the average causal treatment eﬀect can be identiﬁed through the distribution of the observable data (Y, A, X) if assumptions (13.1) and (13.5) hold. This follows because E{Y ∗ (1)} = EX [E{Y ∗ (1)|X}] = EX [E{Y ∗ (1)|A = 1, X}] = EX {E(Y |A = 1, X)},

(13.6) (13.7)

where (13.6) follows from the assumption of no unmeasured confounders and (13.7) follows from the SUTVA assumption. Similarly, we can show that E{Y ∗ (0)} = EX {E(Y |A = 0, X)}. Hence the average causal treatment effect is equal to δ = EX {E(Y |A = 1, X) − E(Y |A = 0, X)},

(13.8)

which only involves the distribution of (Y, A, X). Remark 4. It is important to note that the outer expectation of (13.6) and (13.7) is with respect to the marginal distribution of X and not the conditional distribution of X given A = 1. We must also be a little careful to make sure that, in equations (13.6) and (13.7), we are not conditioning on a null event. One way to ensure that we are not is to assume that both P (A = 1|X = x) and P (A = 0|X = x) are bounded away from zero for all x in the support of X. We will discuss this assumption in greater detail when we introduce the propensity score in the next section.

13.4 Estimating the Average Causal Treatment Eﬀect Regression Modeling We now consider the estimation of the average causal treatment eﬀect from a sample of observed data (Yi , Ai , Xi ), i = 1, . . . , n. The ﬁrst approach, which we refer to as regression modeling, is motivated by equation (13.8). Here, we consider a restricted moment model for the conditional expectation of Y given (A, X) in terms of a ﬁnite-dimensional parameter, say ξ. That is, E(Y |A, X) = µ(A, X, ξ).

(13.9)

The regression model could be as complicated as is deemed necessary by the data analyst to get a good ﬁt. For example, we might consider the linear model with an interaction term; that is, µ(A, X, ξ) = ξ0 + ξ1 A + ξ2 X + ξ3 AX, or, if the response variable Y is positive, we might consider the corresponding log-linear model

13.5 Coarsened-Data Semiparametric Estimators

µ(A, X, ξ) = exp(ξ0 + ξ1 A + ξ2 X + ξ3 AX).

329

(13.10)

The parameter ξ can be estimated using the generalized estimating equations developed in Section 4.6. For example, we may solve the estimating equation n ∂µ(Ai , Xi , ξ)

∂ξ

i=1

V −1 (Ai , Xi ){Yi − µ(Ai , Xi , ξ)} = 0,

(13.11)

where V (Ai , Xi ) = var(Yi |Ai , Xi ), to obtain the estimator ξˆn . If the conditional expectations E(Y |A = 1, X) and E(Y |A = 0, X) were known, then a natural consistent and unbiased estimator of the average causal treatment eﬀect δ, given by (13.8), would be obtained using the empirical average n −1 n {E(Y |A = 1, Xi ) − E(Y |A = 0, Xi )}. (13.12) i=1

Under the assumption that the restricted moment model is correct, a natural estimator for E(Y |A = 1, X) − E(Y |A = 0, X) would then be µ(1, X, ξˆn ) − µ(0, X, ξˆn ). Substituting this into (13.12) yields the estimator for the average causal treatment eﬀect, δˆn = n−1

n

{µ(1, Xi , ξˆn ) − µ(0, Xi , ξˆn )}.

(13.13)

i=1

So, for example, if we posited the log-linear model (13.10), then the estimator for the average causal treatment eﬀect would be given by n −1 ˆ ˆ ˆ ˆ ˆ ˆ ˆ exp{ξ0n + ξ1n + (ξ2n + ξ3n )Xi } − exp(ξ0n + ξ2n Xi ) . δn = n i=1

The consistency and asymptotic normality of the estimator δˆn can be obtained in a straightforward fashion by deriving its inﬂuence function. We leave this as an exercise for the reader to derive. We do note, however, that these asymptotic properties are based on the assumption that the restricted moment model E(Y |A, X) = µ(A, X, ξ) is correctly speciﬁed.

13.5 Coarsened-Data Semiparametric Estimators We now consider how to obtain estimators for the average causal treatment eﬀect by casting the problem as a coarsened-data semiparametric model. Toward that end, we denote the full data to be {Y ∗ (1), Y ∗ (0), X, A}, where Y ∗ (1) and Y ∗ (0) denote the potential outcomes for treatment 1 and treatment 0, respectively, A denotes the treatment assignment, and X denotes the

330

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

vector of baseline covariates. The joint density of the full data can be written as p{y ∗ (1), y ∗ (0), x, a} = p{a|y ∗ (1), y ∗ (0), x}p{y ∗ (1), y ∗ (0), x} = p(a|x)p{y ∗ (1), y ∗ (0), x},

(13.14)

where (13.14) follows from the strong ignorability assumption (13.5). We will put no restrictions on the joint density p{y ∗ (1), y ∗ (0), x} of {Y ∗ (1), Y ∗ (0), X} (i.e., a nonparametric model). Consequently, using the same logic as in Section 5.3, we argue that there is only one full-data inﬂuence function of RAL estimators for δ = E{Y ∗ (1) − Y ∗ (0)}. Letting δ0 denote the true value of δ, the full-data inﬂuence function is given by ϕF {Y ∗ (1), Y ∗ (0), X} = {Y ∗ (1) − Y ∗ (0) − δ0 },

(13.15)

which, of course, is the inﬂuence function for the full-data estimator δˆnF = n−1

n

{Yi∗ (1) − Yi∗ (0)}.

i=1

Although the joint distribution of {Y ∗ (1), Y ∗ (0), X} can be arbitrary, we will assume that P (A = 1|X) can be modeled as π(X, ψ) using a ﬁnite number of parameters ψ. Because treatment assignment A is a binary indicator, the conditional density of A given X is p(a|x) = π(x, ψ)a {1 − π(x, ψ)}1−a . The function P (A = 1|X) is deﬁned as the propensity score, as it reﬂects the propensity that an individual will receive one treatment or the other as a function of the baseline covariates X. The propensity score was ﬁrst introduced by Rosenbaum and Rubin (1983), and the properties have been studied extensively by Rosenbaum and Rubin; see, for example, Rosenbaum and Rubin (1984, 1985), Rosenbaum (1984, 1987), and Rubin (1997). Although the model for the propensity score is not used in deﬁning the full-data estimator for the average causal treatment eﬀect δ, it will play a crucial role when we consider observed-data estimators for δ. In contrast with the full data {Y ∗ (1), Y ∗ (0), X, A}, the observed data are given as the many-to-one transformation of the full data, namely (Y, X, A), where Y = AY ∗ (1) + (1 − A)Y ∗ (0). As such, this is an example of coarsened data, where A plays a role similar to the coarsening variable C, or, more speciﬁcally, to the complete-case indicator R deﬁned in the previous chapters. Remark 5. Throughout the chapters on missing and coarsened data, we always assumed that, with positive probability, the coarsening variable C could take on the value ∞ to denote the case when the full data were observed. For this problem, we never get to observe the full data {Y ∗ (1), Y ∗ (0), X}. We either observe Y ∗ (1) when A = 1 or Y ∗ (0) when A = 0. Nonetheless, we will see a similarity in the semiparametric estimators developed for this problem and those for the missing-data problems developed previously.

13.5 Coarsened-Data Semiparametric Estimators

331

Observed-Data Inﬂuence Functions As with all semiparametric models, the key to ﬁnding estimators is to derive the nuisance tangent space and the space orthogonal to the nuisance tangent space, which, in turn, are used to derive the space of inﬂuence functions. We ﬁrst consider the case when the propensity score P (A = 1|X) is known to us. This may be the case, for example, if we designed a randomized study where treatment was assigned at random with known probabilities that could depend on pretreatment baseline covariates. Using arguments identical to those in Chapters 7 and 8, we can show that the space of observeddata inﬂuence functions of RAL estimators for δ corresponds to the space K−1 {(IF )F }, where (IF )F denotes the space of full-data inﬂuence functions and K : H → HF , given by Deﬁnition 1 of Chapter 7, is the many-to-one linear mapping from the observed-data Hilbert space to the full-data Hilbert space, where, for any h ∈ H, K(h) = E{h(Y, A, X)|Y ∗ (1), Y ∗ (0), X}. Consequently, K−1 {(IF )F } denotes all the functions ϕ(Y, A, X) such that E{ϕ(Y, A, X)|Y ∗ (1), Y ∗ (0), X} = ϕF {Y ∗ (1), Y ∗ (0), X} for any ϕF {Y ∗ (1), Y ∗ (0), X} corresponding to a full-data inﬂuence function. In our problem, there is only one full-data inﬂuence function given by (13.15). Hence, the class of observed-data inﬂuence functions corresponds to functions K−1 {Y ∗ (1) − Y ∗ (0) − δ0 }, which, by Lemma 7.4, are equal to h(Y, A, X) + Λ2 , where h(Y, A, X) is any function that satisﬁes the relationship E{h(Y, A, X)|Y ∗ (1), Y ∗ (0), X} = {Y ∗ (1) − Y ∗ (0) − δ0 },

(13.16)

and Λ2 is the linear subspace in H consisting of elements L2 (Y, A, X) such that E{L2 (Y, A, X)|Y ∗ (1), Y ∗ (0), X} = 0, (13.17) also referred to as the augmentation space. A function h(·) satisfying equation (13.16) is motivated by the inverse propensity weighted full-data inﬂuence function; namely, (1 − A)Y AY − − δ0 . (13.18) h(Y, A, X) = π(X) 1 − π(X) To verify this, consider the conditional expectation of the ﬁrst term on the right-hand side of (13.18); that is,

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13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

E

AY Y ∗ (1), Y ∗ (0), X π(X)

AY ∗ (1) ∗ Y (1), Y ∗ (0), X π(X) Y ∗ (1) = E{A|Y ∗ (1), Y ∗ (0), X} π(X) Y ∗ (1) E(A|X) (13.19) = π(X) Y ∗ (1) = (13.20) π(X) = Y ∗ (1), π(X) =E

where (13.19) follows from the strong ignorability assumption. Also, in order for (13.20) to hold, we must not be dividing 0 by 0. Therefore, we will need the additional assumption that the propensity score P (A = 1|X) = π(X) is strictly greater than zeroalmost everywhere. Similarly, we can show that ∗ ∗ E (1−A)Y = Y ∗ (0) as long as 1 − π(X) is strictly greater 1−π(X) |Y (1), Y (0), X than zero almost everywhere. Therefore, we have proved that the function h(·) deﬁned in (13.18) satisﬁes the relationship (13.16) as long as the propensity score 0 < π(x) < 1, for all x in the support of X. To derive the augmentation space Λ2 , we must ﬁnd all functions L2 (·) of Y, A, X that satisfy (13.17). Because A is a binary indicator, any function of Y, A, X can be written as L2 (Y, A, X) = AL21 (Y, X) + (1 − A)L20 (Y, X)

(13.21)

for arbitrary functions L21 (·) and L20 (·) of Y, X. Hence the conditional expectation of L2 (·), given {Y ∗ (1), Y ∗ (0), X}, is E{L2 (Y, A, X)|Y ∗ (1), Y ∗ (0), X} = E[AL21 {Y ∗ (1), X} + (1 − A)L20 {Y ∗ (0), X}|Y ∗ (1), Y ∗ (0), X] ∗

∗

= π(X)L21 {Y (1), X} + {1 − π(X)}L20 {Y (0), X},

(13.22) (13.23)

where (13.22) follows from the SUTVA assumption and (13.23) follows from the strong ignorability assumption. Therefore, in order for (13.17) to hold, we need π(X) L21 {Y ∗ (1), X}. L20 {Y ∗ (0), X} = − (13.24) 1 − π(X) Since both the left- and right-hand sides of (13.24) denote functions of Y ∗ (1), Y ∗ (0), X, the only way that equation (13.24) can hold is if both L20 (·) and L21 (·) are functions of X alone. In that case, any element of Λ2 must satisfy π(X) L21 (X). (13.25) L20 (X) = − 1 − π(X) Substituting the relationship given by (13.25) into (13.21), we conclude that the space Λ2 consists of functions

13.5 Coarsened-Data Semiparametric Estimators

A − π(X) 1 − π(X)

333

L21 (X) for arbitrary functions L21 (X).

Since 1 − π(X) is strictly greater than zero almost everywhere and L21 (X) is an arbitrary function of X, we can write Λ2 as Λ2 = {A − π(X)}h2 (X) for arbitrary h2 (X) . (13.26) Thus, we have demonstrated that the class of all inﬂuence functions of RAL estimators for δ, when the propensity score π(X) is known, is given by AY (1 − A)Y − − δ 0 + Λ2 , (13.27) π(X) 1 − π(X) where Λ2 is deﬁned by (13.26). The optimal inﬂuence function in this class is the one with the smallest variance or, equivalently, the element with the smallest norm. This is obtained {A − π(X)}h02 (X) by choosing the element AY to be minus the projection of π(X) onto Λ2 . − (1−A)Y 1−π(X) − δ0 We remind the reader that the projection of some arbitrary element ϕ(Y, A, X) onto Λ2 is obtained by ﬁnding the unique element h02 (X) such that E ϕ(Y, A, X) − {A − π(X)}h02 (X) {A − π(X)}h2 (X) = 0

for all functions h2 (X). We denote this as Π[ϕ(Y, A, X)|Λ2 ] = {A−π(X)}h02 (X). As indicated earlier, any function ϕ(·) of Y, A, X can be written as ϕ(Y, A, X) = Aϕ1 (Y, X) + (1 − A)ϕ0 (Y, X). Because projections are linear operators, Π[ϕ(Y, A, X)|Λ2 ] = Π[Aϕ1 (Y, X)|Λ2 ] + Π[(1 − A)ϕ0 (Y, X)|Λ2 ]. We now show how to derive these projections in the following theorem. Theorem 13.1. The projection Π[Aϕ1 (Y, X)|Λ2 ] = {A − π(X)}h01 (X),

(13.28)

where h01 (X) = E{ϕ1 (Y, X)|A = 1, X}, and Π[(1 − A)ϕ0 (Y, X)|Λ2 ] = {A − π(X)}h00 (X),

(13.29)

where h00 (X) = −E{ϕ0 (Y, X)|A = 0, X}. Proof. We ﬁrst consider (13.28). By deﬁnition, Π[Aϕ1 (Y, X)|Λ2 ] is deﬁned as the function {A − π(X)}h01 (X) such that 0 E Aϕ1 (Y, X) − {A − π(X)}h1 (X) {A − π(X)}h(X) = 0,

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13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

or equivalently 2 0 E A{A − π(X)}h(X)ϕ1 (Y, X) − {A − π(X)} h1 (X)h(X) = 0,

(13.30)

for all h(X). By a simple conditioning argument, where we ﬁrst condition on X, we can show that the second term on the left-hand side of (13.30) is equal to 2 0 0 E {A − π(X)} h1 (X)h(X) = E π(X){1 − π(X)}h1 (X)h(X) . (13.31) The ﬁrst term on the left-hand side of (13.30) is also computed through a series of iterated conditional expectations; namely, E A{A − π(X)}h(X)ϕ1 (Y, X) = E E A{A − π(X)}h(X)ϕ1 (Y, X) A, X = E A{A − π(X)}h(X)E{ϕ1 (Y, X)|A = 1, X} = E E A{A − π(X)}h(X)E{ϕ1 (Y, X)|A = 1, X} X = E π(X){1 − π(X)}h(X)E{ϕ1 (Y, X)|A = 1, X} . (13.32) Substituting (13.31) and (13.32) into (13.30), we obtain that 0 E π(X){1 − π(X)} E{ϕ1 (Y, X)|A = 1, X} − h1 (X) h(X) = 0

(13.33)

for all h(X). Since π(X) and 1−π(X) are both bounded away from zero almost surely, and because E{ϕ1 (Y, X)|A = 1, X} − h01 (X) is a function of X, then (13.33) can only be true for all h(X) if and only if E{ϕ1 (Y, X)|A = 1, X} − h01 (X) is identically equal to zero; i.e., h01 (X) must equal E{ϕ1 (Y, X)|A = 1, X}, thus proving (13.28). The proof of (13.29) follows similarly. Returning to the class of inﬂuence functions of RAL estimators for δ given by (13.27), the eﬃcient inﬂuence function in this class is given by AY AY (1 − A)Y (1 − A)Y − − δ0 − Π − − δ0 Λ2 , π(X) 1 − π(X) π(X) 1 − π(X) which by Theorem 13.1 is equal to {A − π(X)}E(Y |A = 1, X) AY − π(X) π(X) {A − π(X)}E(Y |A = 0, X) (1 − A)Y − − δ0 . − 1 − π(X) 1 − π(X)

(13.34)

13.5 Coarsened-Data Semiparametric Estimators

335

Motivated by the eﬃcient inﬂuence function (13.34), an estimator for δ would be obtained as the solution to the estimating equation n {Ai − π(Xi )}µ(1, Xi ) Ai Yi − π(X ) π(Xi ) i i=1 (1 − Ai )Yi {Ai − π(Xi )}µ(0, Xi ) − − − δ = 0, 1 − π(Xi ) 1 − π(Xi ) or, more speciﬁcally, n {Ai − π(Xi )}µ(1, Xi ) Ai Yi − π(Xi ) π(Xi ) i=1 {Ai − π(Xi )}µ(0, Xi ) (1 − Ai )Yi − , − 1 − π(Xi ) 1 − π(Xi )

δˆn =n−1

(13.35)

where µ(A, X) = E(Y |A, X). Of course, µ(A, X) is not known to us but can be estimated by positing a model where E(Y |A, X) = µ(A, X, ξ) as we did in (13.9). The parameter ξ can be estimated by solving the estimating equation (13.11), and then µ(1, Xi , ξˆn ) and µ(0, Xi , ξˆn ) can be substituted into (13.35) to obtain a locally eﬃcient estimator for δ. The development above assumed that the propensity score π(X) was known to us. In observational studies, this will not be the case. Consequently, we must posit a model for the propensity score, say assuming that P (A = 1|X) = π(X, ψ).

(13.36)

Since A is binary, the logistic regression model is often used; i.e., π(X, ψ) =

exp(ψ0 + ψ1T X) . 1 + exp(ψ0 + ψ1T X)

The estimator for ψ would be obtained using maximum likelihood; i.e., ψˆn is the value of ψ that maximizes the likelihood n !

π(Xi , ψ)Ai {1 − π(Xi , ψ)}(1−Ai ) .

i=1

Consequently, in an observational study, the locally eﬃcient semiparametric estimator for the average causal treatment eﬀect would be given by n Ai Yi {Ai − π(Xi , ψˆn )}µ(1, Xi , ξˆn ) −1 ˆ δn = n − ˆ π(Xi , ψn ) π(Xi , ψˆn ) i=1 (1 − Ai )Yi {Ai − π(Xi , ψˆn )}µ(0, Xi , ξˆn ) − − . (13.37) 1 − π(Xi , ψˆn ) 1 − π(Xi , ψˆn )

336

13 Double-Robust Estimator of the Average Causal Treatment Eﬀect

Double Robustness We complete this chapter by demonstrating that the locally eﬃcient semiparametric estimator for δ, given by (13.37), is doubly robust. That is, under the SUTVA and strong ignorability assumptions, (13.37) is a consistent estimator for δ if either the model for the propensity score π(X, ψ) or the regression model E(Y |A, X) = µ(A, X, ξ) is correct. We use the conventions that p p ψˆn − → ψ ∗ and ξˆn − → ξ ∗ to denote that, under suitable regularity conditions, these estimators will converge whether the corresponding model is correct or not and denote, by letting ψ ∗ = ψ0 or ξ ∗ = ξ0 , the case when these estimators converge to the truth; i.e., that the corresponding model is correctly speciﬁed. Because the estimator (13.37) is a sample average, it is easy to show that it will converge in probability to AY {A − π(X, ψ ∗ )}µ(1, X, ξ ∗ ) E − ∗ π(X, ψ ) π(X, ψ ∗ ) {A − π(X, ψ ∗ )}µ(0, X, ξ ∗ ) (1 − A)Y − . (13.38) − 1 − π(X, ψ ∗ ) 1 − π(X, ψ ∗ ) By the SUTVA assumption, AY {A − π(X, ψ ∗ )}Y ∗ (1) AY ∗ (1) = = Y ∗ (1) + , ∗ ∗ π(X, ψ ) π(X, ψ ) π(X, ψ ∗ )

(13.39)

(1 − A)Y (1 − A)Y ∗ (0) {A − π(X, ψ ∗ )}Y ∗ (0) ∗ = = Y . (0) − 1 − π(X, ψ ∗ ) 1 − π(X, ψ ∗ ) 1 − π(X, ψ ∗ )

(13.40)

and

Substituting (13.39) and (13.40) into (13.38) yields E{Y ∗ (1) − Y ∗ (0)} {A − π(X, ψ ∗ )}{Y ∗ (1) − µ(1, X, ξ ∗ )} +E π(X, ψ ∗ ) ∗ {A − π(X, ψ )}{Y ∗ (0) − µ(0, X, ξ ∗ )} +E . 1 − π(X, ψ ∗ )

(13.41) (13.42) (13.43)

Since E{Y ∗ (1)−Y ∗ (0)} of (13.41) is equal to δ, the proof of double robustness will follow if we can show that the expectations in (13.42) and (13.43) are equal to zero if either ψ ∗ = ψ0 or ξ ∗ = ξ0 . Let us ﬁrst assume that the propensity score is correctly speciﬁed; i.e., ψ ∗ = ψ0 . We compute the expectation of (13.42) by iterated conditional expectations, where we ﬁrst condition on Y ∗ (1) and X to obtain that (13.42) equals [E{A|Y ∗ (1), X} − π(X, ψ0 )]{Y ∗ (1) − µ(1, X, ξ ∗ )} . E π(X, ψ0 )

13.6 Exercises for Chapter 13

337

Because of the strong ignorability assumption, E{A|Y ∗ (1), X} = E(A|X) = π(X, ψ0 ), thus allowing us to conclude that (13.42) equals zero when ψ ∗ = ψ0 . A similar argument can also be used to show that (13.43) equals zero when ψ ∗ = ψ0 . Now consider the case where the regression model E(Y |A, X) is correctly speciﬁed; i.e., ξ ∗ = ξ0 . Again, we compute the expectation of (13.42) by iterated conditional expectations, but now we ﬁrst condition on A and X to obtain that (13.42) equals {A − π(X, ψ ∗ )}[E{Y ∗ (1)|A, X} − µ(1, X, ξ0 )] . E π(X, ψ ∗ ) Because of SUTVA, µ(1, X, ξ0 ) = E(Y |A = 1, X) = E{Y ∗ (1)|A = 1, X}, and because of the strong ignorability assumption, E{Y ∗ (1)|X} = E{Y ∗ (1)|A, X} = E{Y ∗ (1)|A = 1, X} = µ(1, X, ξ0 ), thus allowing us to conclude that (13.42) equals zero when ξ ∗ = ξ0 . A similar argument can also be used to show that (13.43) equals zero when ξ ∗ = ξ0 . The local semiparametric eﬃciency together with the double-robustness property makes the estimator (13.37) desirable as compared with, say, the regression estimator (13.13), whose consistency is completely dependent on correctly modeling the regression relationship E(Y |A, X). Remark 6. The connection of the propensity score to causality has been studied carefully by Rosenbaum, Rubin, and others; see, for example, Rosenbaum and Rubin (1983, 1984, 1985), Rosenbaum (1984, 1987), and Rubin (1997). Diﬀerent methods for estimating the average causal treatment eﬀect using propensity scores have been advocated. These include stratiﬁcation, matching, and inverse propensity weighting. The locally eﬃcient estimator derived above is sometimes referred to as the augmented inverse propensity weighted estimator. This estimator was compared with other commonly used estimators of the average causal treatment eﬀect in a series of numerical simulations by Lunceford and Davidian (2004), who generally found that this estimator performs the best across a wide variety of scenarios.

13.6 Exercises for Chapter 13 1. Derive the inﬂuence function for the regression estimator δˆn given by equation (13.13). 2. Derive the inﬂuence function for the augmented inverse propensity weighted estimator δˆn given by equation (13.37).

14 Multiple Imputation: A Frequentist Perspective

A popular approach for dealing with missing data is the use of multiple imputation, which was ﬁrst introduced by Rubin (1978b). Although most of this book has focused on semiparametric models, where the model includes inﬁnite-dimensional nuisance parameters, this chapter will only consider ﬁnitedimensional parametric models, as in Chapter 3. Because of its importance in missing-data problems, we conclude with a discussion of this methodology. Imputation methods, where we replace missing values by some “best guess” and then analyze the data as if complete, have a great deal of intuitive appeal. However, unless one is careful about how the imputation is carried out and how the subsequent inference is made, imputation methods may lead to biased estimates with estimated conﬁdence intervals that are too narrow. Rubin’s proposal for multiple imputation allowed the use of this intuitive idea in a manner that results in correct inference. Rubin’s justiﬁcation is based on a Bayesian paradigm. In this chapter, we will consider the statistical properties of multiple-imputation estimators from a frequentist point of view using large sample theory. Much of the development is based on two papers, by Wang and Robins (1998) and Robins and Wang (2000). Although most of the machinery developed in the previous chapters that led to AIPWCC estimators will not be used here, we feel that this topic is of suﬃcient importance to warrant study in its own right. As we have all along, we will consider a full-data model, where the full data are denoted by Z1 , . . . , Zn assumed iid with density pZ (z, β), where β is a ﬁnite-dimensional parameter, say q-dimensional. The observed (coarsened) data will be assumed coarsened at random and denoted by {Ci , GCi (Zi )}, i = 1, . . . , n. Remark 1. When data are coarsened at random and the full-data parameter β is ﬁnite-dimensional, then β can be estimated using maximum likelihood. The coarsened-data likelihood was derived in (7.10), where we showed that β could be estimated by maximizing

340

14 Multiple Imputation: A Frequentist Perspective n !

pGri (Zi ) (gri , β),

(14.1)

i=1

where pGr (Z) (gr , β) =

pZ (z, β)dνZ (z). {z:Gr (z)=gr }

However, even for parametric models, maximizing coarsened-data likelihoods may be diﬃcult to implement. Multiple imputation is an attempt to use simpler methods that are easier to implement. We will assume throughout that the full-data maximum likelihood estimator for β can be derived easily using standard software. We will also assume that the standard large-sample properties of maximum likelihood estimators apply to the full-data model. For example, denoting the full-data score vector by ∂ log pZ (z, β) S F (z, β) = , ∂β the MLE βˆnF is obtained by solving the likelihood equation n

S F (Zi , β) = 0.

i=1

Remark 2. On notation In this chapter, we only consider the parameter β with no additional nuisance parameters. Therefore, the full-data score vector will be denoted by S F (Z, β) (without the subscript β used in the previous chapters). As usual, when we use the notation S F (Z, β), we are considering a q-dimensional vector of functions of Z and β. If the score vector is evaluated at the truth, β = β0 , then this will often be denoted by S F (Z) = S F (Z, β0 ). A similar convention will be used when we consider the observed-data score vector, which will be denoted as S{C, GC (Z), β}, and, at the truth, as S{C, GC (Z), β0 } or S{C, GC (Z)}. Under suitable regularity conditions, which will be assumed throughout, D F (β0 ) , n1/2 (βˆnF − β0 ) −→ N 0, Veﬀ F where {Veﬀ (β0 )}−1 is the full-data information matrix, which we denote by T F I (β0 ) = E{S F (Z)S F (Z)}. That is,

' (−1 F Veﬀ (β0 ) = I F (β0 ) . We present an illustrative example where multiple-imputation methods may be used.

14 Multiple Imputation: A Frequentist Perspective

341

Example 1. Surrogate marker problem Consider the logistic regression model used to model the probability of a dichotomous response as a function of the covariates X. Letting Y = {1, 0} denote a binary response variable, we assume P (Y = 1|X) =

exp(θT X) . 1 + exp(θT X)

(14.2)

With full data (Yi , Xi ), i = 1, . . . , n, the maximum likelihood estimator for θ can be obtained in a straightforward fashion using standard software. We wish to consider the problem where the variable X, or a subset of X, is expensive to obtain. For instance, X may represent some biological marker, derived from stored plasma collected on everyone, that is expensive to measure. However, another variable, W , is identiﬁed as a cheaper surrogate for X. That is, W is correlated with X and satisﬁes the surrogacy assumption; namely, P (Y = 1|X, W ) = P (Y = 1|X) =

exp(θT X) . 1 + exp(θT X)

Let us also assume that (X, W ) follows a multivariate normal distribution σXX σXW X µX ∼N , T . W µW σXW σW W In an attempt to save costs, the inexpensive surrogate variable W will be collected on everyone in the sample, as will the response variable Y . However, the expensive variable X will be obtained on only a validation subsample of individuals in the study using stored blood chosen at random with a prespeciﬁed probability of selection that may depend on (Y, W ) (i.e., missing at random). Letting R denote the indicator of a complete-case, then the observed data are denoted as (Ri , Yi , Wi , Ri Xi ), i = 1, . . . , n. Although the primary focus is the parameter θ, since the model is ﬁnitedimensional, we will not diﬀerentiate between the parameters of interest and the nuisance parameters. Hence β = (θT , µTX , µTW , σXX , σXW , σW W )T . This example will be used for illustration later. With observed data, the parameter β can be estimated eﬃciently by maximizing the observed-data likelihood (14.1). That is, we would ﬁnd the solution to the observed-data likelihood equation n i=1

S{Ci , GCi (Zi ), β} = 0,

(14.3)

342

14 Multiple Imputation: A Frequentist Perspective

where the observed-data score vector is given by S{Ci , GCi (Zi ), β} = E{S F (Zi , β)|Ci , GCi (Zi ), β}.

(14.4)

Remark 3. It is important for some of the subsequent developments that we introduce the following notation. When we write E{S F (Zi , β )|Ci , GCi (Zi ), β }, where we allow β , β to be arbitrary values not necessarily at the truth or equal to each other, we mean speciﬁcally that E{S F (Zi , β )|Ci = ri , GCi (Zi ) = gri , β } S F (z, β )pZ (z, β )dνZ (z) =

{z:Gri (z)=gri }

pZ (z, β )dνZ (z)

.

(14.5)

{z:Gri (z)=gri }

Unless otherwise stated, expectations and conditional expectations will be evaluated with parameter values at the truth. Consequently, the observed-data score vector, evaluated at the truth, is S{C, GC (Z)} = E{S F (Z)|C, GC (Z)} = E{S F (Z, β0 )|C, GC (Z), β0 }. Therefore, when we derive the observed-data MLE by solving (14.3), we are speciﬁcally looking for βˆn that satisﬁes n i=1

E{S F (Zi , βˆn )|Ci , GCi (Zi ), βˆn } = 0. ↑ ↑ notice that these two must be equal

Again, under suitable regularity conditions, D 1/2 ˆ n (βn − β0 ) −→ N 0, Veﬀ (β0 ) , where {Veﬀ (β0 )}−1 is the observed-data information matrix, denoted by I(β0 ) = E S{Ci , GC (Zi )}S T {Ci , GCi (Zi )} .

14.1 Full- Versus Observed-Data Information Matrix Because coarsened data Gr (Z) represent a many-to-one transformation of the full data Z (i.e., a data reduction), it seems intuitively clear that the asymptotic variance of the full-data MLE βˆnF will be smaller than the asymptotic

14.1 Full- Versus Observed-Data Information Matrix

343

variance of the observed-data MLE βˆn . In this section, we give a formal proof of this result. T Let var{S F (Z)} = E{S F (Z)S F (Z)} denote that variance matrix of S F (Z). Then the asymptotic variance of the full-data MLE βˆnF is given by F (β0 ) = [var{S F (Z)}]−1 . Similarly, the asymptotic variance of the observedVeﬀ data MLE βˆn is Veﬀ (β0 ) = (var[S{C, GC (Z)}])−1 . We now give two results regarding the full-data and observed-data estimators for β. Theorem 14.1. The observed-data information matrix is smaller than or equal to the full-data information matrix; that is, var[S{C, GC (Z)}] ≤ var{S F (Z)}, where the notation “≤” means that var{S F (Z)} − var[S{C, GC (Z)}] is nonnegative deﬁnite. Proof. By the law of conditional variance, ' ( ' ( var {S F (Z)} = var E S F (Z)|C, GC (Z) + E var S F (Z)|C, GC (Z) ' ( = var [S{C, GC (Z)}] + E var S F (Z)|C, GC (Z) . (14.6) This implies that var{S F (Z)} − var[S{C, GC (Z)}] is nonnegative deﬁnite. Theorem 14.2. The asymptotic variance of the full-data MLE is smaller than or equal to the asymptotic variance of the observed-data MLE; that is, F Veﬀ (β0 ) ≤ Veﬀ (β0 ).

(14.7)

Proof. This follows from results about inﬂuence functions; namely, if we deﬁne −1 F FT ϕF (Z) S F (Z) eﬀ (Z) = E S (Z)S and −1 ϕeﬀ {C, GC (Z)} = E S{C, GC (Z)}S T {C, GC (Z)} S{C, GC (Z)}, then F ϕeﬀ {C, GC (Z)} = ϕF eﬀ (Z) + ϕeﬀ {C, GC (Z)} − ϕeﬀ (Z) . We also note that E S F (Z)S T {C, GC (Z)} = E E S F (Z)S T {C, GC (Z)}|C, GC (Z) = E S{C, GC (Z)}S T {C, GC (Z)} . (14.8)

344

14 Multiple Imputation: A Frequentist Perspective

Equation (14.8) can be used to show that T F E(ϕF eﬀ (Z) ϕeﬀ {C, GC (Z)} − ϕeﬀ (Z) ) = 0, which, in turn, can be used to show that F var[ϕeﬀ {C, GC (Z)}] = var{ϕF eﬀ (Z)} + var( ϕeﬀ {C, GC (Z)} − ϕeﬀ (Z) ) or var[ϕeﬀ {C, GC (Z)}] − var{ϕF eﬀ (Z)} is nonnegative deﬁnite. The proof is complete upon noticing that var[ϕeﬀ {C, GC (Z)}] = (var[S{C, GC (Z)}])−1 = Veﬀ (β0 ) and ' F ( −1 F = Veﬀ (β0 ). var{ϕF eﬀ (Z)} = [var S (Z) ] For many problems, working with the observed (coarsened) data likelihood may be diﬃcult, with no readily available software. For such instances, it may be useful to ﬁnd methods where we can use the simpler full-data inference to analyze coarsened data. This is what motivated much of the inverse weighted methodology discussed in the previous chapters. Multiple imputation is another such methodology popularized by Rubin (1978b, 1987). See also the excellent overview paper by Rubin (1996).

14.2 Multiple Imputation Multiple imputation is implemented as follows. For each observed-data value {Ci , GCi (Zi )}, we sample at random from the conditional distribution pZ|C,GC (Z) {z|Ci , GCi (Zi )} m times to obtain random quantities Zij , j = 1, . . . , m, i = 1, . . . , n. These are the imputation of the full data from the observed (coarsened) data. The “j”-th set of imputed full data, denoted by Zij , i = 1, . . . , n, is used to ∗ obtain the j-th estimator βˆnj by solving the full-data likelihood equation: n i=1

∗ S F (Zij , βˆnj ) = 0.

(14.9)

14.2 Multiple Imputation

345

That is, we use the j-th imputed data set and treat these data as if they were ∗ full data to obtain the full-data MLE βˆnj . The proposed multiple-imputation estimator is βˆn∗ = m−1

m

∗ . βˆnj

(14.10)

j=1

Rubin (1987) argues that, under appropriate conditions, this estimator is consistent and asymptotically normal. That is, D

n1/2 (βˆn∗ − β0 ) −→ N (0, Σ∗ ), ˆ∗ = where the asymptotic variance Σ∗ can be estimated by Σ "−1 ∗ ) ∂S F (Zij , βˆnj n − m ∂β T j=1 i=1 * +, / 0/ 0T m ˆ∗ − βˆ∗ βˆ∗ − βˆ∗ β n n nj nj j=1 m+1 n + . m m−1 * +, −1

m

−1

n

We note that the ﬁrst term in the sum is an average of the estimators of the full-data asymptotic variance using the inverse of the full-data observed information matrix over the imputed full-data sets and the second term is the sample variance of the imputation estimators multiplied by a ﬁnite “m” correction factor. Remark 4. If we knew the true value, β0 , then we could generate a random variable Zij (β0 ) whose distribution has density pZ (z, β0 ) by ﬁrst generating a random {Ci , GCi (Zi )} from the distribution with density pC,GC (Z) (r, gr , β0 ) and then generating a random Zij (β0 ) from the conditional distribution with conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi ), β0 }. This is essentially the logic that motivates multiple imputation. Assuming the model is correct, the observed data {Ci , GCi (Zi )} are generated, by nature, from the density pC,GC (Z) (r, gr , β0 ). However, since we don’t know the true value of β0 , the Zij must be generated from the conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi ), β}, where β must be estimated from the data in some fashion. Such imputed values are referred to as Zij (β). We will consider two cases.

346

14 Multiple Imputation: A Frequentist Perspective

(a) In what we will denote as frequentist imputation, an initial estimator βˆnI is obtained from the coarsened data. The imputation Zij (βˆnI ) are obtained by sampling from pZ|C,GC (Z) {z|Ci , GCi (Zi ), βˆnI }. The resulting estimator is referred to by Wang and Robins (1998) as a type B estimator. Rubin (1987) also refers to this type of imputation as improper imputation. (b) For Bayesian imputation (the approach advocated by Rubin), the Zij are generated from the predictive distribution pZ|C,GC (Z) {z|Ci , GCi (Zi )}. The predictive distribution is given by pZ|C,GC (Z) {z|Ci , GCi (Zi ), β} pβ|C,GC (Z) {β|Ci , GCi (Zi )}dνβ (β) . +, * This is the posterior distribution of β given the observed data In order to implement the Bayesian approach, which Rubin refers to as proper imputation, one needs to specify a prior distribution for β, from which the posterior distribution pβ|C,GC (Z) {β|Ci , GCi (Zi )} is computed using Bayes’s rule. Then (i) we randomly select β (j) from the posterior distribution and, (ii) conditional on β (j) , we sample from pZ|C,GC (Z) {z|Ci , GCi (Zi ), β (j) } to obtain Zij (β (j) ). A multiple-imputation estimator using this approach is referred to by Wang and Robins (1998) as a type A estimator.

14.3 Asymptotic Properties of the Multiple-Imputation Estimator In what follows, we will derive the asymptotic properties of the multipleimputation estimator. We ﬁrst will consider the “frequentist” approach, which imputes from the conditional distribution ﬁxing the parameter β at some initial estimator βˆnI . Some discussion of Bayesian proper imputation will also be considered later. In order to implement multiple imputation from a frequentist view, we must start with some initial estimator for β, say βˆnI . For missing-data problems, an initial estimator can often be obtained using only the complete cases (i.e., {i : Ci = ∞}). For example, when the data are missing completely at random, we can obtain a consistent asymptotically normal estimator using only

14.3 Asymptotic Properties of the Multiple-Imputation Estimator

347

the complete cases since these are a representative sample (albeit smaller) from the population. If the data are missing at random (MAR), we might use an inverse probability weighted complete-case estimator as an initial value. The initial estimator for β will be assumed to be an RAL estimator; that is, n n1/2 (βˆnI − β0 ) = n−1/2 q{Ci , GCi (Zi )} + op (1), i=1

where q{Ci , GCi (Zi )} is the i-th inﬂuence function of the estimator βˆnI . We remind the reader of the following properties of inﬂuence functions for RAL estimators: (a) The eﬃcient inﬂuence function, denoted by ϕeﬀ {Ci , GCi (Zi )}, equals −1 E S{Ci , GCi (Zi )}S T {Ci , GCi (Zi )} S{Ci , GCi (Zi )}. (b) For any inﬂuence function of an RAL estimator q{Ci , GCi (Zi )}, E q{Ci , GCi (Zi )}S T {Ci , GCi (Zi )} = *+,I q×q .

(14.11)

(14.12)

the identity matrix (c) The inﬂuence function q{Ci , GCi (Zi )} can be written as q{Ci , GCi (Zi )} = ϕeﬀ {Ci , GCi (Zi )} + h{Ci , GCi (Zi )},

(14.13)

where h{Ci , GCi (Zi )} has mean zero and E h{Ci , GCi (Zi )}S T {Ci , GCi (Zi )} = 0q×q . Hence var [q{Ci , GCi (Zi )}] = var [ϕeﬀ {Ci , GCi (Zi )}] + var [h{Ci , GCi , (Zi )}] −1 = E S{Ci , GCi (Zi )}S T {Ci , GCi (Zi )} + var [h{Ci , GCi (Zi )}] . (14.14) In studying the asymptotic properties of n1/2 (βˆn∗ − β0 ), we will consider the following questions. 1. Is the distribution asymptotically normal? 2. What is its asymptotic variance? 3. Can we estimate the asymptotic variance? How well does Rubin’s variance estimator work? 4. What are the eﬃciency properties of the multiple-imputation estimator? We begin the study of the asymptotic properties by ﬁrst introducing some preliminary lemmas. Rigorous proofs of these lemmas require careful analysis

348

14 Multiple Imputation: A Frequentist Perspective

and the use of empirical processes that are beyond the scope of this book. We will, however, provide some heuristic justiﬁcation of the results. As we have emphasized repeatedly throughout this book, the key to the asymptotic properties of RAL estimators is being able to derive their inﬂuence function. We will derive the inﬂuence function of the multiple-imputation estimator through a series of approximations. We begin by giving the ﬁrst such approximation. Lemma 14.1. The multiple-imputation estimator βˆn∗ deﬁned by (14.9) and (14.10) using the imputed values Zij (βˆnI ), j = 1, . . . , m, i = 1, . . . , n, where βˆnI denotes some initial estimator, can be approximated as

n1/2 (βˆn∗ − β0 ) = n−1/2

n '

⎡ ⎤ m ( −1 ⎣m−1 S F {Zij (βˆnI ), β0 }⎦ + op (1), I F (β0 )

i=1

j=1

(14.15) where I F (β0 ) is the full-data information matrix; namely, −∂S F (Z, β0 ) F FT E = E S (Z, β )S (Z, β ) . 0 0 ∂β T Proof. Heuristic sketch ∗ is the solution to the equation The j-th imputation estimator βˆnj n

∗ S F {Zij (βˆnI ), βˆnj } = 0,

i=1

which by the mean value expansion is equal to $ # n n / 0 ∂S F {Zij (βˆI ), β˜nj } n ˆ∗ − β0 , × β S F {Zij (βˆnI ), β0 } + nj ∂β T i=1 i=1 ∗ where β˜nj is an intermediate value between βˆnj and β0 . Under suitable regularity conditions,

−n−1

n ∂S F {Zij (βˆI ), β˜nj } i=1

n ∂β T

∂S F {Zij (β0 ), β0 } P , − →E − ∂β T

(14.16)

which, by standard results from likelihood theory, is the information matrix, which is also equal to T E S F {Zij (β0 ), β0 }S F {Zij (β0 ), β0 } = I F (β0 ). Therefore,

14.3 Asymptotic Properties of the Multiple-Imputation Estimator

n

1/2

/

n 0 ∗ 1/2 ˆ βnj − β0 = n {I F (β0 )}−1 S F {Zij (βˆnI ), β0 } + op (1),

349

(14.17)

i=1

which implies that n1/2 (βˆn∗ −β0 ) = n−1/2

n '

⎡ ⎤ m ( −1 ⎣m−1 I F (β0 ) S F {Zij (βˆnI ), β0 }⎦+op (1).

i=1

j=1

As a consequence of Lemma 14.1, we have shown that n1/2 (βˆn∗ − β0 ), up to order op (1), is proportional to ⎡ ⎤ n m ⎣m−1 n−1/2 S F {Zij (βˆnI ), β0 }⎦ . (14.18) i=1

j=1

However, this does not give us the desired inﬂuence function for βˆn∗ because Zij (βˆnI ) is evaluated at a random quantity (βˆnI ) that involves data from all individuals and therefore (14.18) is not a sum of iid terms. In order to ﬁnd the inﬂuence function, we write (14.18) as the sum of ⎡ ⎤ n m ⎣m−1 n−1/2 S F {Zij (β0 ), β0 }⎦ (14.19) i=1

+ n−1/2

n i=1

j=1

⎡ ⎣m−1

m j=1

S F {Zij (βˆnI ), β0 } − m−1

m

⎤ S F {Zij (β0 ), β0 }⎦

(14.20)

j=1

and then show how these two pieces can be expressed approximately as a sum of iid terms. Remark 5. We remind the reader that, for a ﬁxed β, Zij (β), j = 1, . . . , m, i = 1, . . . , n are obtained through a two-stage process. For any i, nature (sampling from the population) provides us with the data {Ci , GCi (Zi )} derived from the distribution pC,GC (Z) (r, gr , β0 ). Then, for j = 1, . . . , m, the data analyst draws m values at random from the conditional distribution pZ|C,GC (Z) {z|Ci , GCi (Zi ), β}. Consequently, the vector {Zi1 (β), . . . , Zim (β)} is made up of correlated random variables, but these random vectors across i are iid random vectors. Also, because of the way the data are generated, the marginal distribution of Zij (β) is the same for all i and j. If β = β0 , then the marginal distribution of Zij (β0 ) has density pZ (z, β0 ) (i.e., the density for the full data at the truth). However, if β = β0 , then the marginal density for Zij (β) is more complex. Based on the discussion in Remark 5, (14.19) is made up of a sum of n iid elements, where the i-th element of the sum is equal to

350

14 Multiple Imputation: A Frequentist Perspective −1

m

m

S F {Zij (β0 ), β0 },

(14.21)

j=1

i = 1, . . . , n. In addition, the distribution of S F {Zij (β0 ), β0 } is the same as the distribution of S F (Zi , β0 ), where Zi is the full data. Therefore, E[S F {Zij (β0 ), β0 }] = E{S F (Zi , β0 )} = 0 for all i, j, which implies that (14.21) has mean zero. Hence, (14.19) is a normalized sum of mean-zero iid random vectors that will converge to a normal distribution by the central limit theorem. We now consider the approximation of (14.20) as a sum of iid random vectors. This is given by the following theorem. Theorem 14.3. The expression (14.20) is equal to n−1/2

n '

( I F (β0 ) − I(β0 ) q{Ci , GCi (Zi )} + op (1),

(14.22)

i=1

where I F (β0 ) is the full-data information matrix, T −∂S F (Z, β0 ) E = E S F (Z, β0 )S F (Z, β0 ) , T ∂β I(β0 ) is the observed-data information matrix, ∂S{C, GC (Z), β0 } E − = E S{C, GC (Z), β0 }S T {C, GC (Z), β0 } , ∂β T and q{Ci , GCi (Zi )} is the i-th inﬂuence function of the initial estimator βˆnI . Theorem 14.3 will be proved using a series of lemmas. Lemma 14.2. Let Zij (β) denote a random draw from the conditional distribution with conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi ), β}. If we deﬁne λ(β, β0 ) = E S F {Zij (β), β0 } , (14.23) then

∂λ(β, β0 ) ∂β T

= I F (β0 ) − I(β0 ). β=β0

Proof. By the law of conditional expectations, (14.23) equals E E S F {Zij (β), β0 }|Ci , GCi (Zi ), β .

(14.24)

Because Zij (β) is a random draw from the conditional distribution with conditional density

14.3 Asymptotic Properties of the Multiple-Imputation Estimator

351

pZ|C,GC (Z) {z|Ci , GCi (Zi ), β}, this means that the inner expectation of (14.24) is equal to S F (z, β0 )pZ|C,GC (Z) {z|Ci , GCi (Zi ), β}dνZ|C,GC (Z) {z|Ci , GCi (Zi )}. ↑ note the value β here The outer expectation of (14.24), however, involves the distribution of the observed data {Ci , GC (Zi )}, which are generated from the truth, β0 . Therefore, the overall expectation is given as F S (z, β0 )pZ|C,GC (Z) (z|r, gr , β)dνZ|C,GC (Z) (z|r, gr ) λ(β, β0 ) = × pC,GC (Z) (r, gr , β0 )dνC,GC (Z) (r, gr ).

(14.25)

Because pZ|C,GC (Z) (z|r, gr , β)pC,GC (Z) (r, gr , β)dνZ|C,GC (Z) (z|r, gr )dνC,GC (Z) (r, gr ) = pC,Z (r, z, β)dνC,Z (r, z), we can write (14.25) as pC,Z (r, z, β) S F (z, β0 ) pC,GC (Z) (r, gr , β0 )dνC,Z (r, z). pC,GC (Z) (r, gr , β)

(14.26)

Taking the derivative of (14.26) with respect to β and evaluating at β0 , we obtain ∂λ(β, β0 ) pC,Z (r, z, β) ∂ F = S (z, β ) 0 ∂β T ∂β T pC,GC (Z) (r, gr , β) β=β0 β=β0

=

S T (z, β0 )

× pC,GC (Z) (r, gr , β0 )dνC,Z (r, z) ∂ log pC,Z (r, z, β) pC,Z (r, z, β0 ) pC,GC (Z) (r, gr , β0 ) ∂β T pC,GC (Z) (r, gr , β)

× pC,GC (Z) (r, gr , β0 )dνC,Z (r, z).

β=β0

(14.27)

Because the data are coarsened at random, pZ (z, β) pC,Z (r, z, β) = . pC,GC (Z) (r, gr , β) pGr (Z) (gr , β) Therefore pC,Z (r, z, β) ∂ log ∂β T pC,GC (Z) (r, gr , β)

= β=β0

(14.28)

∂ log pZ (z, β0 ) ∂ log pGr (Z) (gr , β0 ) − . ∂β T ∂β T (14.29)

352

14 Multiple Imputation: A Frequentist Perspective

When data are coarsened at random, we showed in Lemma 7.2 that ( ' ∂ log pGr (Z) (gr , β0 ) = E S F (Z)|C = r, GC (Z) = gr = S(r, gr ). T ∂β Therefore, (14.29) is equal to T

S F (z, β0 ) − S T (r, gr , β0 ). Substituting this last result into (14.27) and rearranging some terms yields T S F (z, β0 ) S F (z, β0 ) − S T (r, gr , β0 ) pC,Z (r, z, β0 )dνC,Z (r, z) T = E S F (Z, β0 )S F (Z, β0 ) − E S F (Z, β0 )S T {C, GC (Z), β0 } . (14.30) Using (14.8), we obtain that ( ' E S F (Z, β0 )S T (C, GC (Z), β0 ) = I(β0 ). Hence, (14.30) equals I F (β0 ) − I(β0 ), proving Lemma 14.2. Remark 6. The result of Lemma 14.2 can be deduced, with slight modiﬁcation, from equation (6) on page 480 of Oakes (1999). The term I F (β0 ) − I(β0 ) is also referred to as the “missing information,” as this is the information that is lost due to the coarsening of the data. Before giving the next lemma, we ﬁrst give a short description of the notion of “stochastic equicontinuity.” Stochastic Equicontinuity Consider the centered stochastic process ⎡ ⎤ n m ⎣m−1 Wn (β) = n−1/2 S F {Zij (β), β0 } − λ(β, β0 )⎦ i=1

j=1

as a process in β, where λ(β, β0 ) = E S F {Zij (β), β0 } . Using the theory of empirical processes (see van der Vaart and Wellner, 1996), under suitable regularity conditions, the process Wn (β) converges weakly to a tight Gaussian process. When this is the case, we have stochastic equicontinuity, where, for every ε, η > 0, there exists a δ > 0 and an n0 such that " P

sup β ,β : β −β <δ

Wn (β ) − Wn (β ) ε

≤η

for all n > n0 , where “ · ” denotes the usual Euclidean norm or distance.

14.3 Asymptotic Properties of the Multiple-Imputation Estimator

353

Lemma 14.3. If stochastic equicontinuity holds, then Wn (βˆnI ) − Wn (β0 ) = op (1) whenever βˆnI is a consistent estimator for β0 . Proof. Choose arbitrary ε, η > 0. Then, by stochastic equicontinuity, there exists a δ(ε, η) and n(ε, η) such that " P

sup β ,β : β −β <δ(ε,η)

Wn (β ) − Wn (β ) ε

≤ η/2

for all n > n(ε, η). By consistency, there exists for every δ and η an n∗ (δ, η) such that P ( βˆnI − β0 δ) ≤ η/2 for all n > n∗ (δ, η). Note that the event

βˆnI − β0 < δ(ε, η) and

"

sup β ,β : β −β <δ(ε,η)

Wn (β ) − Wn (β ) < ε

⊆ Wn (βˆnI ) − Wn (β0 ) < ε . By taking complements, we obtain

P { Wn (βˆnI ) − Wn (β0 ) ε} # ≤ P { βˆI − β0 δ(ε, η)} n

∪

"$ sup

β ,β : β −β <δ(ε,η)

Wn (β ) − Wn (β ) ε

≤ P { βˆnI − β0 δ(ε, η)} +P

sup β ,β : β −β <δ(ε,η)

"

Wn (β ) − Wn (β ) ε .

By choosing n > max[n(ε, η), n∗ {δ(ε, η), η}], we obtain P { Wn (βˆnI ) − Wn (β0 ) ε} ≤ η/2 + η/2 = η. Lemma 14.4. The statistic (14.20), namely ⎡ ⎤ n m m ⎣m−1 n−1/2 S F {Zij (βˆnI ), β0 } − m−1 S F {Zij (β0 ), β0 }⎦ , i=1

is equal to

j=1

j=1

n1/2 {λ(βˆnI , β0 ) − λ(β0 , β0 )} + op (1).

354

14 Multiple Imputation: A Frequentist Perspective

Proof. This follows directly as a consequence of Lemma 14.3, which states that Wn (βˆnI ) − Wn (β0 ) = op (1), after some simple rearrangement of terms.

Lemma 14.5. ∂λ(β, β0 ) n1/2 {λ(βˆnI , β0 ) − λ(β0 , β0 )} = ∂β T

n1/2 (βˆnI − β0 ) + op (1). β=β0

Proof. Follows from a standard delta method argument. We now return to the proof of Theorem 14.3. Proof of Theorem 14.3 Lemmas 14.4 and 14.5 imply that (14.20) is equal to ∂λ(β, β0 ) ∂β T

n1/2 (βˆnI − β0 ) + op (1). β=β0

The proof is complete because of Lemma 14.2, which states that ∂λ(β, β0 ) ∂β T

= I F (β0 ) − I(β0 ), β=β0

and the deﬁnition of the inﬂuence function of βˆnI , n1/2 (βˆnI − β0 ) = n−1/2

n

q{Ci , GCi (Zi )} + op (1).

i=1

14.4 Asymptotic Distribution of the Multiple-Imputation Estimator We are now in a position to derive the asymptotic distribution of βˆn∗ , which we present in the following theorem: Theorem 14.4. Letting βˆn∗ denote the multiple imputation estimator and denoting the i-th inﬂuence function of the initial estimator by (14.13), i.e., q{Ci , GCi (Zi )} = ϕeﬀ {Ci , GCi (Zi )} + h{Ci , GCi (Zi )}, where ϕeﬀ {Ci , GCi (Zi )} is deﬁned by (14.11), then D n1/2 (βˆn∗ − β0 ) −→ N (0, Σ∗ ) ,

where Σ∗ is equal to

14.4 Asymptotic Distribution of the Multiple-Imputation Estimator

355

' (−1 ' F (' (−1 −1 I (β0 ) − I(β0 ) I F (β0 ) {I(β0 )} + m−1 I F (β0 ) ' (−1 ' F ( I (β0 ) − I(β0 ) var [h{Ci , GCi (Zi )}] + I F (β0 ) ' F (' (−1 I (β0 ) − I(β0 ) I F (β0 ) . (14.31) Proof. As a result of Lemma14.1, Theorem 14.3, and equations (14.19) and (14.20), we have shown that

n1/2 (βˆn∗ − β0 ) = n−1/2

n '

⎛⎡ ⎤ m ( −1 ⎝⎣m−1 I F (β0 ) S F {Zij (β0 ), β0 }⎦

i=1

j=1

⎞ ( + I F (β0 ) − I(β0 ) q{Ci , GCi (Zi )}⎠ + op (1). '

(14.32)

This is a key result, as we have identiﬁed the inﬂuence function for the multiple-imputation estimator as the i-th element in the summand in (14.32). Asymptotic normality follows from the central limit theorem. The asymptotic variance of n1/2 (βˆn∗ − β0 ) is the variance of the inﬂuence function, which we will now derive in a series of steps. Toward that end, we ﬁrst compute ⎤ ⎡ m S F {Zij (β0 ), β0 }⎦ . (14.33) var ⎣m−1 j=1

Computing (14.33) Using the law for iterated conditional variance, (14.33) can be written as ⎛ ⎡ ⎤⎞ m E ⎝var ⎣m−1 S F {Zij (β0 ), β0 } Ci , GCi (Zi )⎦⎠ (14.34) ⎛ ⎡

j=1

+ var ⎝E ⎣m−1

m

⎤⎞ S F {Zij (β0 ), β0 } Ci , GCi (Zi )⎦⎠ .

(14.35)

j=1

Because, conditional on {Ci , GCi (Zi )}, the Zij (β0 ), j = 1, . . . , m are independent draws from the conditional distribution with conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi ), β0 }, this means that the conditional variance ⎤ ⎡ m S F {Zij (β0 ), β0 } Ci , GCi (Zi )⎦ var ⎣m−1 j=1

is equal to

356

14 Multiple Imputation: A Frequentist Perspective

m−1 var

'

( S F (Zi , β0 )|Ci , GCi (Zi ) .

Therefore, (14.34) is equal to ' ( m−1 E var S F (Zi , β0 )|Ci , GCi (Zi ) . In equation (14.6), we showed ' ( E var S F (Zij β0 )|Ci , GCi (Zi ) = I F (β0 ) − I(β0 ). Consequently,

( ' (14.34) = m−1 I F (β0 ) − I(β0 ) .

(14.36)

Similar logic can be used to show ⎤ ⎡ m S F {Zij , (β0 ), β0 } Ci , GCi (Zi )⎦ E ⎣m−1 '

j=1

( = E S F (Zi , β0 )|Ci , GCi (Zi ) = S{Ci , GCi (Zi )}.

(14.37)

Therefore (14.35) is equal to var [S{Ci , GCi (Zi )}] = I(β0 ). Combining (14.36) and (14.38) gives us that ' ( (14.33) = m−1 I F (β0 ) − I(β0 ) + I(β0 ).

(14.38)

(14.39)

Next we compute the covariance matrix ⎞ ⎛⎡ ⎤ T m ' ( E ⎝⎣m−1 S F {Zij (β0 ), β0 }⎦ I F (β0 ) − I(β0 ) q{Ci , GCi (Zi )} ⎠ . j=1

(14.40) Computing (14.40) Expression (14.40) can be written as m−1

m

' ( E S F {Zij (β0 ), β0 }q T {Ci , GCi (Zi )} I F (β0 ) − I(β0 ) ,

j=1

where

E S F {Zij (β0 ), β0 }q T {Ci , GCi (Zi )} = E E S F {Zij (β0 ), β0 }q T {Ci , GCi (Zi )}|Ci , GCi (Zi ) E E S F {Zij (β0 ), β0 }|Ci , GCi (Zi ) q T {Ci , GCi (Zi )} * +, we showed in (14.37) that this equals S{Ci , GCi (Zi )}

= E S{Ci , GCi (Zi )}q T {Ci , GCi (Zi )} = I q×q (identity matrix) by (14.12).

14.4 Asymptotic Distribution of the Multiple-Imputation Estimator

357

Therefore (14.40) = I F (β0 ) − I(β0 ).

(14.41)

Finally, var {I F (β0 ) − I(β0 )}q{Ci , GCi (Zi )} ( ' ( ' (14.42) = I F (β0 ) − I(β0 ) var [q{Ci , GCi (Zi )}] I F (β0 ) − I(β0 ) = {I F (β0 ) − I(β0 )} I −1 (β0 ) + var [h{Ci , GCi (Zi )}] {I F (β0 ) − I(β0 )}, (14.43) where (14.43) follows from (14.14). Using (14.39), (14.41), and (14.43), and after some straightforward algebraic manipulation, we are able to derive the variance matrix for the inﬂuence function of βˆn∗ (i.e., the i-th summand in (14.32)) to be Σ∗ = {I(β0 )}−1 ' (−1 ' F (' (−1 + m−1 I F (β0 ) I (β0 ) − I(β0 ) I F (β0 ) ' (−1 ' F ( ' + I F (β0 ) I − I(β0 ) var[h{Ci , GCi (Zi )}] I F (β0 ) ' (−1 −I(β0 )} I F (β0 ) . This completes the proof of Theorem 14.4.

Examining (14.32), we conclude that the inﬂuence function for the multipleimputation estimator, with m imputation draws, is equal to m {I F (β0 )}−1 m−1 S F {Zj (β0 ), β0 } + {I F (β0 ) − I(β0 )}q{C, GC (Z)} , j=1

(14.44) where Zj (β0 ) denotes the j-th random draw from the conditional distribution of pZ|C,GC (Z) {z|C, GC (Z), β0 }. As a consequence of the law of large numbers, we would expect, under suitable regularity conditions, that m−1

m

P

S F {Zj (β0 ), β0 } − → E{S F (Z)|C, GC (Z)} = S{C, GC (Z)}

j=1

as m → ∞. Speciﬁcally, in order to prove that m−1

m j=1

it suﬃces to show that

P

S F {Zj (β0 ), β0 } − S{C, GC (Z)} − → 0,

358

14 Multiple Imputation: A Frequentist Perspective

2 m m→∞ −1 F E m S {Zj (β0 ), β0 } − S{C, GC (Z)} −−−−→ 0.

(14.45)

j=1

Computing the expectation above by ﬁrst conditioning on {C, GC (Z)}, we obtain that 2 m ' F ( −1 F −1 E m S {Zj (β0 ), β0 }−S{C, GC (Z)} = E m var S (Z)|C, GC (Z) . j=1

( ' So, for example, if the conditional variance var S F (Z)|C, GC (Z) is bounded almost surely, then (14.45) would hold. Consequently, as m → ∞, the inﬂuence function of the multiple-imputation estimator (14.44) converges to F −1 F {I (β0 )} S{C, GC (Z)} + {I (β0 ) − I(β0 )}q{C, GC (Z)} . (14.46) As we will now demonstrate, the limit, as m → ∞, of the multipleimputation estimator is related to the expectation maximization (EM) algorithm. The EM algorithm, ﬁrst introduced by Dempster, Laird, and Rubin (1977) and studied carefully by Wu (1983), is a numerical method for ﬁnding the MLE that is especially useful with missing-data problems. The EM algorithm is an iterative procedure where the expectation of the full-data loglikelihood is computed with respect to a current value of a parameter estimate and then an updated estimate for the parameter is obtained by maximizing (j) the expected full-data log-likelihood. Hence, if βˆn is the value of the estimator at the j-th iteration, then the (j + 1)-th value of the estimator is obtained by solving the estimating equation n E S F (Z, βˆn(j+1) )|Ci , GCi (Zi ), βˆn(j) = 0.

(14.47)

i=1

We now show the relationship between the EM algorithm and the multipleimputation estimator in the following theorem. Theorem 14.5. Let the one-step updated EM estimator, βˆnEM , be the solution to n (14.48) E S F (Z, βˆnEM )|Ci , GCi (Zi ), βˆnI = 0, i=1

βˆnI

where is the initial estimator for β, whose i-th inﬂuence function is q{Ci , GCi (Zi )}, which was used for imputation. The inﬂuence function of βˆnEM is identically equal to (14.46), the limiting inﬂuence function for the multipleimputation estimator as m → ∞. Proof. A simple expansion of βˆnEM about β0 , keeping βˆnI ﬁxed, in (14.48) yields

14.4 Asymptotic Distribution of the Multiple-Imputation Estimator

0=n

−1/2

359

n F I ˆ E S (Z, β0 )|Ci , GCi (Zi ), βn i=1

F n ∂S (Z, β0 ) −1 Ci , GCi (Zi ), β0 n1/2 (βˆnEM − β0 ) + op (1). E + n T ∂β i=1 Because F n ∂S (Z, β0 ) n Ci , GCi (Zi ), β0 E ∂β T i=1 F ∂S (Z, β0 ) P Ci , GCi (Zi ), β0 − →E E ∂β T F ∂S (Z, β0 ) = −I F (β0 ), =E ∂β T −1

we obtain n1/2 (βˆnEM −β0 ) = {I F (β0 )}−1 n−1/2

n E S F (Z, β0 )|Ci , GCi (Zi ), βˆnI +op (1). i=1

(14.49) An expansion of βˆnI about β0 on the right-hand side of (14.49) yields n−1/2

n E S F (Z, β0 )|Ci , GCi (Zi ), βˆnI i=1

= n−1/2 + n

n E S F (Z, β0 )|Ci , GCi (Zi ), β0

i=1 n −1 i=1

∂ E{S F (Z, β0 )|Ci , GCi (Zi ), β} ∂β T

(14.50) n1/2 (βˆnI − β0 ) + op (1). β=β0

The sample average is n ∂ F n−1 E{S (Z, β )|C , G (Z ), β} 0 i Ci i ∂β T β=β0 i=1 ∂ P F , − →E E{S (Z, β )|C , G (Z ), β} 0 i C i i ∂β T β=β0

(14.51)

where ∂ E{S F (Z, β0 )|Ci , GCi (Zi ), β} ∂β T β=β0 $ # F S (z, β0 )p(z, β)dν(z) ∂ z:GCi (z)=GCi (Zi ) = ∂β T p(z, β)dν(z) z:GC (z)=GC (Zi ) i

i

. β=β0

(14.52)

360

14 Multiple Imputation: A Frequentist Perspective

Straightforward calculations yield that the derivative in (14.52) is equal to var{S F (Z)|Ci , GCi (Zi )} and hence (14.51) is equal to F E var{S (Z)|Ci , GCi (Zi )} = I F (β0 ) − I(β0 ), (14.53) where the last equality follows from (14.6). Using (14.4), we obtain that (14.50) is equal to n n−1/2 S{Ci , GCi (Zi )}. (14.54) i=1

By the deﬁnition of an inﬂuence function, n1/2 (βˆnI − β0 ) = n−1/2

n

q{Ci , GCi (Zi )} + op (1).

(14.55)

i=1

Combining equations (14.49)–(14.55), we conclude that the inﬂuence function of βˆnEM is equal to (14.46), thus concluding the proof. In (14.13), we expressed the inﬂuence function of βˆnI as the sum of {I(β0 )}−1 S{C, GC (Z)} + h{C, GC (Z)},

(14.56)

where h{C, GC (Z)} is orthogonal to S{C, GC (Z)}; i.e., E(hS T ) = 0q×q . Substituting (14.56) for q{C, GC (Z)} in (14.46), we obtain another representation of the inﬂuence function for βˆEM as n

ϕeﬀ {C, GC (Z)} + {I F (β0 )}−1 {I F (β0 ) − I(β0 )}h{C, GC (Z)}, where

(14.57)

ϕeﬀ {C, GC (Z)} = {I(β0 )}−1 S{C, GC (Z)}

is the eﬃcient observed-data inﬂuence function. We note that the inﬂuence function in (14.57) is that for a one-step updated EM estimator that started with the initial estimator βˆnI . Using similar logic, we would conclude that the EM algorithm after j iterations would yield an EM(j) with inﬂuence function estimator βˆn ϕeﬀ {C, GC (Z)} + J j h{C, GC (Z)}, where J is the q × q matrix {I F (β0 )}−1 {I F (β0 ) − I(β0 )}. For completeness, we now show that J j will converge to zero (in the sense that all elements in the matrix will converge to zero) as j goes to inﬁnity, thus demonstrating that the EM algorithm will converge to the eﬃcient observed-data estimator. This proof is taken from Lemma A.1 of Wang and Robins (1998).

14.4 Asymptotic Distribution of the Multiple-Imputation Estimator

361

Proof. Both I F (β0 ) and {I F (β0 ) − I(β0 )} are symmetric positive-deﬁnite matrices. From Rao (1973, p. 41), we can express I F (β0 ) = RT R and {I F (β0 ) − I(β0 )} = RT ΛR, where R is a nonsingular matrix and Λ is a diagonal matrix. Moreover, because I(β0 ) = I F (β0 ) − {I F (β0 ) − I(β0 )} = RT (I q×q − Λ)R is positive deﬁnite, this implies that all the elements on the diagonal of Λ must be strictly less than 1. Consequently, J j = (R)−1 Λj R will converge to zero as j → ∞. Remarks regarding the asymptotic variance 1. The asymptotic variance of the observed-data eﬃcient estimator for β is {I(β0 )}−1 . Because (14.31) is greater than {I(β0 )}−1 , this implies that the multiple-imputation estimator is not fully eﬃcient. 2. Even if the initial estimator βˆnI is eﬃcient (i.e., h{Ci , GCi (Zi )} = 0), the resulting multiple-imputation estimator loses some eﬃciency due to the contribution to the variance of the second term of (14.31), but this loss of eﬃciency vanishes as m → ∞. 3. For any initial estimator βˆnI , as the number of multiple-imputation draws “m” gets larger, the asymptotic variance decreases (from the contribution of the second term of (14.31)). Thus the resulting estimator becomes more eﬃcient with increasing m. 4. We showed in Theorem 14.5 that as m goes to inﬁnity, the resulting multiple-imputation estimator is equivalent to a one-step update of an EM algorithm. This suggests that one strategy is that after m imputations, we start the process again, now using βˆn∗ as the initial estimator. By continuing this process, we can iterate toward full eﬃciency. To implement such a strategy, m must be chosen suﬃciently large to ensure that the new estimator is more eﬃcient than the previous one. As we converge toward eﬃciency, m must get increasingly large. 5. Other algebraically equivalent representations of the asymptotic variance, which we will use later, are given by '

and

' (−1 ( ' ( I F (β0 ) I(β0 ) + m−1 I F (β0 ) − I(β0 ) + 2 I F (β0 ) − I(β0 ) ' ( ' ( + I F (β0 ) − I(β0 ) var[q{Ci , GCi (Zi )}] I F (β0 ) − I(β0 ) ' F (−1 I (β0 ) (14.58)

362

14 Multiple Imputation: A Frequentist Perspective

'

(−1 ' F (' (−1 m+1 ' F I (β0 ) I (β0 ) − I(β0 ) I F (β0 ) m ' F (−1 ' F ( + I (β0 ) I (β0 ) − I(β0 ) var[q{Ci , GCi (Zi )}] ' F (' (−1 I (β0 ) − I(β0 ) I F (β0 ) . (14.59)

(−1 I F (β0 ) +

14.5 Estimating the Asymptotic Variance In this section, we will consider estimators for the asymptotic variance of the frequentist (type B) multiple-imputation estimator. Although Rubin (1987) refers to this type of imputation as improper and does not advocate using his intuitive variance estimator in such cases, my experience has been that, in practice, many statisticians do not distinguish between proper and improper imputation and will often use Rubin’s variance formula. Therefore, we begin this section by studying the properties of Rubin’s variance formula when used with frequentist multiple imputation. Rubin suggested the following estimator for the asymptotic variance of n1/2 (βˆn∗ − β0 ): −1

m

+

m

# n

j=1

m+1 m

−1

n ∗ } −∂S F {Zij (βˆnI ), βˆnj i=1

$−1

∂β T

m ∗ ∗ (βˆnj − βˆn∗ )(βˆnj − βˆn∗ )T n . m−1 j=1

(14.60)

It is easy to see that the ﬁrst term in (14.60) converges in probability to ∂S(Z, β0 ) E − = {I F (β0 )}−1 . ∂β T Results regarding the second term of (14.60) are given in the following theorem. Theorem 14.6. ⎧ ⎫ m / ⎨ 0/ 0T ⎬ n→∞ ∗ ∗ E n βˆnj − βˆn∗ βˆnj − βˆn∗ −−−−→ ⎩ ⎭ j=1

' (−1 ' F (' (−1 (m − 1) I F (β0 ) I (β0 ) − I(β0 ) I F (β0 ) . Proof. Examining the inﬂuence function of n1/2 (βˆn∗ − β0 ), derived in (14.32), we conclude that

14.5 Estimating the Asymptotic Variance

363

1 ∗ ∗ n1/2 (βˆnj − βˆn∗ ) = n 2 (βˆnj − β0 ) − n1/2 (βˆn∗ − β0 ) n (−1 F ' F = n−1/2 I (β0 ) S {Zij (β0 ), β0 } − S¯iF (β0 )

i=1

+ op (1), where S¯iF (β0 ) = m−1

m

S F {Zij (β0 ), β0 }.

j=1

(Notice that the terms involving q{Ci , GCi (Zi )} in (14.32) cancel out.) Therefore, ∗ ∗ n(βˆnj − βˆn∗ )(βˆnj − βˆn∗ )T = % n & ' (−1 F −1 F F ¯ n I (β0 ) S {Zij (β0 ), β0 } − Si (β0 ) i=1

& % n ( ' T −1 + op (1). S F {Zij (β0 ), β0 } − S¯iF (β0 ) I F (β0 ) × i=1

Because the quantities inside the two sums above are independent across i = 1, . . . , n, we obtain ∗ ∗ E{n(βˆnj − βˆn∗ )(βˆnj − βˆn∗ )T } → /' (−1 F E I F (β0 ) S {Zij (β0 ), β0 } − S¯iF (β0 ) T ' F (−1 0 × S F {Zij (β0 ), β0 } − S¯iF (β0 ) I (β0 )

and

⎫ ⎧ m ⎬ ⎨ ∗ ∗ ∗ E n (βˆnj − βˆnj )(βˆnj − βˆn∗ )T → ⎭ ⎩ j=1 ⎛ m ' F F (−1 I (β0 ) E⎝ S {Zij (β0 ), β0 } − S¯iF (β0 ) j=1

F T S {Zij (β0 ), β0 } − S¯iF (β0 ) The expectation in (14.61) is evaluated by

' (−1 × I F (β0 ) .

(14.61)

364

14 Multiple Imputation: A Frequentist Perspective

⎞ m F T S {Zij (β0 ), β0 } − S¯iF (β0 ) SβF {Zij (β0 ), β0 } − S¯iF (β0 ) ⎠ E⎝ ⎛

j=1

=E

m

T

S F {Zij (β0 ), β0 }S F {Zij (β0 ), β0 }

(14.62)

j=1

−

m m T 1 F S {Zij (β0 ), β0 }S F {Zij (β0 ), β0 } . E m j=1 j =1

When j = j , T T E[S F {Zij (β0 ), β0 }S F {Zij (β0 ), β0 }] = E S F (Zi , β0 )S F (Zi , β0 ) = I F (β0 ), whereas when j = j , T E S F {Zij (β0 ), β0 }S F {Zij (β0 ), β0 } = cov S F {Zij (β0 ), β0 }, S F {Zij (β0 ), β0 } = E cov S F {Zij (β0 ), β0 }, S F {Zij (β0 ), β0 }|Ci , GCi (Zi ) (14.63) F F + cov E S {Zij (β0 ), β0 }|Ci , GCi (Zi ) , E S {Zij (β0 ), β0 }|Ci , GCi (Zi ) . Because, conditional on Ci , GCi (Zi ), the Zij (β0 ) are independent draws for diﬀerent j, from the conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi )}, this means that the ﬁrst term of (14.63) (conditional covariance) is zero. Because E[S F {Zij (β0 ), β0 }|Ci , GCi (Zi )] = S{Ci , GCi (Zi )} for all j = 1, . . . , m, then the second term of (14.63) is var[S{Ci , GCi (Zi )}] = I(β0 ). Thus, from these results, we obtain that (14.62) equals ( 1 ' F mI (β0 ) + m(m − 1)I(β0 ) m ' ( = (m − 1) I F (β0 ) − I(β0 ) .

mI F (β0 ) −

(14.64)

Finally, using (14.61), we obtain ⎧ ⎫ m / ⎨ 0/ 0T ⎬ ∗ ∗ E n → βˆnj − βˆn∗ βˆnj − βˆn∗ ⎩ ⎭ j=1

' (−1 ' F (' (−1 (m − 1) I F (β0 ) I (β0 ) − I(β0 ) I F (β0 ) . This completes the proof of Theorem 14.6.

(14.65)

Consequently, Rubin’s estimator for the variance, (14.60), is an unbiased (asymptotically) estimator for

14.5 Estimating the Asymptotic Variance

{I F (β0 )}−1 +

m+1 m

'

365

(−1 ' F (' (−1 I F (β0 ) I (β0 ) − I(β0 ) I F (β0 ) .

Comparing this with the asymptotic variance of n1/2 (βˆn∗ −β0 ) given by (14.59), we see that Rubin’s formula underestimates the asymptotic variance for the frequentist type B multiple-imputation estimator. Remark 7. We wish to note that the ﬁrst term in Rubin’s variance estimator is indeed a consistent estimator for {I F (β0 )}−1 ; that is, it converges in probability as n → ∞. The second term, however, namely m ∗ ∗ (βˆnj − βˆn∗ )(βˆnj − βˆn∗ )T m+1 n , m m−1 j=1 is an asymptotically unbiased estimator for (−1 ' F (' (−1 m+1 ' F I (β0 ) I (β0 ) − I(β0 ) I F (β0 ) . m That is, the expectation converges as n → ∞ for m ﬁxed. However, this second term is not a consistent estimator but rather converges to a proper distribution. Consistency is only obtained by also letting m → ∞. Nonetheless, a consistent estimator for the asymptotic variance of n1/2 (βˆn∗ − β0 ) can be derived as follows. Consistent Estimator for the Asymptotic Variance Because βˆnI was assumed to be an RAL estimator for β with inﬂuence function q{C, GC (Z)}, this implies that n1/2 (βˆnI − β0 ) → N 0, var [q{C, GC (Z)}] . Suppose we have a consistent estimator for the asymptotic variance of our ˆ GC (Z)}]. Then, if we can initial estimator βˆnI , which we denote as var[q{C, construct consistent estimators for I F (β0 ) and I(β0 ), we can substitute these into (14.59) to obtain a consistent estimator of the asymptotic variance of the multiple-imputation estimator. As we already discussed in the context of Rubin’s variance formula, a consistent estimator for I F (β0 ) can be derived by # $ m n ∗ ∂S F {Zij (βˆnI ), βˆnj } F −1 −1 ˆ . (14.66) In (β0 ) = −m n ∂β T j=1 i=1 +, * This is the observed information matrix, which is often derived for us in the j-th imputed full-data analysis

366

14 Multiple Imputation: A Frequentist Perspective

A consistent estimator for I(β0 ) can be obtained by Iˆn (β0 ) = n−1

n

{m(m − 1)}

i=1

T ∗ × S F {Zij (βˆnI ), βˆnj } .

−1

∗ S F {Zij (βˆnI ), βˆnj }

j,j =1,...,m j=j

(14.67)

This follows directly from (14.63). Another estimator for {I F (β0 ) − I(β0 )} is motivated from the relationship (14.6), which states that F F I (β0 ) − I(β0 ) = E var S {Zij (β0 ), β0 }|Ci , GCi (Zi ) . This suggests using n−1

n i=1

(m − 1)−1

T − S¯βFi (βn∗ )

m ∗ ∗ S F {Zij (βˆnI ), βˆnj } − S¯βFi (βˆn∗ ) S F {Zij (βˆnI ), βˆnj } j=1

(14.68)

as an estimator for I F (β0 ) − I(β0 ).

14.6 Proper Imputation In Bayesian imputation, the j-th imputation is obtained by sampling from pZ|C,GC (Z) {z|Ci , GCi (Zi ), β (j) }, where β (j) itself is sampled from some distribution. Rubin (1978b) suggests sampling β (j) from the posterior distribution p{β|Ci , GCi (Zi )}. This is equivalent to drawing the imputation from the predictive distribution p{z|Ci , GCi (Zi )}. Remark 8. This logic seems a bit circular since Bayesian inference is based on deriving the posterior distribution of β given the observed data. Under suitable regularity conditions on the choice of the prior distribution, the posterior mean or mode of β is generally an eﬃcient estimator for β. Therefore, using proper imputation, where we draw from the posterior distribution of β, we start with an eﬃcient estimator and, after imputing m full data sets, we end up with an estimator that is not eﬃcient. When the sample size is large, the posterior distribution of the parameter and the sampling distribution of the estimator are closely approximated by each other. The initial estimator βˆnI was assumed to be asymptotically normal; that is, 1/2 ˆI n (β − β0 ) ∼ N 0, var[q{C, GC (Z)}] , n

14.6 Proper Imputation

367

where q{C, GC (Z)} denotes the inﬂuence function of βˆnI . Therefore, mimicking the idea of Bayesian imputation, instead of ﬁxing the values βˆnI for each of the m imputations, at the j-th imputation, we sample β (j) from var[q{C, ˆ GC (Z)}] , N βˆnI , n where var[q{C, ˆ GC (Z)}] is a consistent estimator for the asymptotic variance, and then randomly choose Zij (β (j) ) from the conditional distribution with conditional density pZ|C,GC (Z) {z|Ci , GCi (Zi ), β (j) }. Remark 9. If βˆnI were eﬃcient, say the MLE, then this would approximate sampling the β’s from the posterior distribution and the Z’s from the predictive distribution. Using this approach, the j-th imputed estimator is the solution to the equation n

∗ S F {Zij (β (j) ), βˆnj } = 0,

i=1

and the ﬁnal multiple-imputation estimator is βˆn∗ = m−1

m

∗ . βˆnj

j=1

Therefore, if we decide to use such an approach, the obvious questions are 1. What is the asymptotic distribution of n1/2 (βˆn∗ − β0 )? 2. How does it compare with improper imputation? 3. How do we estimate the asymptotic variance? ˆ∗ − β0 ) Asymptotic Distribution of n1/2 (β n Using the same expansion that led us to (14.15), we again obtain that ⎡ ⎤ n m ' ( −1 ⎣m−1 n1/2 (βˆn∗ − β0 ) = n−1/2 I F (β0 ) S F {Zij (β (j) ), β0 }⎦ + op (1). * +, i=1

j=1

note here the dependence on j

(14.69)

Also, the same logic that was used for the multiple-imputation (improper) estimator leads us to the relationship

368

14 Multiple Imputation: A Frequentist Perspective

n−1/2

n

⎡ ⎣m−1

i=1

m

⎤ S F {Zij (β (j) ), β0 }⎦

j=1

= n−1/2

n

⎡

⎣m−1

i=1

m

⎤ S F {Zij (β0 ), β0 }⎦

j=1

m ( −1 + I (β0 ) − I(β0 ) m n1/2 (β (j) − β0 ) + op (1).

'

F

(14.70)

j=1

Note that (14.70) can be written as ⎡ ⎤ n m ⎣m−1 S F {Zij (β0 ), β0 }⎦ n−1/2 i=1

j=1

m ' ( + I F (β0 ) − I(β0 ) m−1 n1/2 (β (j) − βˆnI )

'

j=1

( + I (β0 ) − I(β0 ) n1/2 (βˆnI − β0 ) + op (1). F

(14.71)

n Because n1/2 (βˆnI −β0 ) = n−1/2 q{Ci , GCi (Zi )}+op (1), we can write (14.71) i=1

as n−1/2

n

⎛⎡ ⎝⎣m−1

i=1

m

⎤

⎞ ( S F {Zij (β0 ), β0 }⎦ + I F (β0 ) − I(β0 ) q{Ci , GCi (Zi )}⎠ '

j=1

(14.72) + m−1

m '

( I F (β0 ) − I(β0 ) n1/2 (β (j) − βˆnI ) + op (1).

(14.73)

j=1

Note that (14.72) is a term that was derived when we considered type B multiple-imputation estimators in the previous section (improper imputation), whereas (14.73) is an additional term due to sampling the β (j) ’s from var[q{C ˆ i , GCi (Zi )}] N βˆnI , . n Therefore ' (−1 n1/2 (βˆn∗ − β0 ) = [ I F (β0 ) {(14.72) + (14.73)}]. The expression (14.72) multiplied by {I F (β0 )}−1 is, up to op (1), identical to representation (14.32) of the type B multiple-imputation estimator and hence is asymptotically normally distributed with mean zero and variance equal to (14.59).

14.6 Proper Imputation

369

By construction, n1/2 (β (j) − βˆnI ), j = 1, . . . , m are m independent draws from a normal distribution with mean zero and variance var[q{C ˆ i , GCi (Zi )}]. Because var{q(·)} ˆ is a consistent estimator for var{q(·)}, this implies that n1/2 (β (j) − βˆnI ), j = 1, . . . , m is asymptotically equivalent to V1 , . . . , Vm , which are iid normal random variables with mean zero and variance var[q{C, GC (Z)}] and independent of all the data {Ci , GCi (Zi )}, i = 1, . . . , n. Therefore, n1/2 (βˆn∗ − β0 ) = n−1/2

n

⎛⎡

{I F (β0 )}−1 ⎝⎣m−1

i=1

m

⎤ S F {Zij (β0 ), β0 }⎦

j=1

⎞ ( + I F (β0 ) − I(β0 ) q{Ci , GCi (Zi )}⎠ '

+ m−1

m

( ' {I F (β0 )}−1 I F (β0 ) − I(β0 ) Vj

(14.74)

(14.75)

j=1

+ op (1). Because (14.74) converges to a normal distribution with mean zero and variance matrix (14.59), and (14.75) is distributed as normal with mean zero and variance matrix ' (−1 ' F ( m−1 I F (β0 ) I (β0 ) − I(β0 ) var[q{C, GC (Z)}] ' (' (−1 × I F (β0 ) − I(β0 ) I F (β0 ) (14.76) and is independent of (14.74), this implies that n1/2 (βˆn∗ −β0 ) is asymptotically normal with mean zero and asymptotic variance equal to (14.59) + (14.76), which equals '

(−1 (−1 ' F (' (−1 m+1 ' F I (β0 ) I (β0 ) I (β0 ) − I(β0 ) I F (β0 ) + m (−1 ' F ( m+1 ' F I (β0 ) + I (β0 ) − I(β0 ) var[q{C, GC (Z)}] m ' F (' (−1 × I (β0 ) − I(β0 ) I F (β0 ) . (14.77) F

Comparing (14.77) with (14.59), we see that the estimator using “proper” imputation has greater variance (is less eﬃcient) than the corresponding “improper” imputation estimator, which ﬁxes βˆnI at each imputation. This makes intuitive sense since we are introducing additional variability by sampling the β’s from some distribution at each imputation. The increase in the variance is given by (14.76). The variances of the two methods converge as m goes to inﬁnity.

370

14 Multiple Imputation: A Frequentist Perspective

Let us now study the properties of Rubin’s formula for the asymptotic variance when applied to type A (proper imputation) multiple-imputation estimators. Rubin’s Estimator for the Asymptotic Variance Using arguments that led us to (14.74) and (14.75), we obtain that ∗ n1/2 (βˆnj − βˆn∗ ) = n−1/2

n

{I F (β0 )}−1 [S F {Zij (β0 ), β0 } − S¯iF (β0 )]

i=1

+ {I (β0 )}−1 {I F (β0 ) − I(β0 )}(Vj − V¯ ) + op (1), F

m where V¯ = m−1 j=1 Vj . Therefore, ⎧ ⎫ m / ⎨ 0/ 0T ⎬ n→∞ ∗ ∗ E n βˆnj − βˆn∗ βˆnj − βˆn∗ −−−−→ ⎩ ⎭ '

j=1

(−1 I (β0 ) × ⎧ ⎛ ⎞ m ⎨ F F F F T E ⎝ [S {Zij (β0 ), β0 } − S¯i (β0 )][S {Zij (β0 ), β0 } − S¯i (β0 )] ⎠ ⎩ j=1 ⎫ ⎧ ⎫ m ⎬' ⎬ ' F ( ⎨ ( + I (β0 ) − I(β0 ) E I F (β0 ) − I(β0 ) (Vj − V¯ )(Vj − V¯ )T ⎭ ⎩ ⎭ F

'

(−1 × I F (β0 ) .

j=1

(14.78)

Remark 10. The term involving q{Ci , GCi (Zi )} is common for all j and hence ∗ drops out when considering {βˆnj − βˆn∗ }. Also, the additivity of the expectations in (14.78) is a consequence of the fact that Vj are generated independently from all the data. We showed in (14.64) that ⎛ ⎞ m E ⎝ [S F {Zij (β0 ), β0 } − S¯iF (β0 )][S F {Zij (β0 ), β0 } − S¯iF ]T ⎠ j=1

' ( = (m − 1) I F (β0 ) − I(β0 ) .

Also E

⎧ m ⎨ ⎩

j=1

(Vj − V¯ )(Vj − V¯ )T

⎫ ⎬ ⎭

= (m − 1)var[q{C, GC (Z)}].

(14.79)

(14.80)

14.7 Surrogate Marker Problem Revisited

371

Therefore, substituting (14.79) and (14.80) into (14.78) yields ⎫ ⎧ m / ⎨ 0/ 0T ⎬ ∗ ∗ → E n βˆnj − βˆn∗ βˆnj − βˆn∗ ⎭ ⎩ j=1 ' F (−1 ' F ( ' ( (m − 1) I (β0 ) I (β0 ) − I(β0 ) + I F (β0 ) − I(β0 ) ' F ( ' F (−1 × var [q{C, GC (Z)}] I (β0 ) − I(β0 ) I (β0 ) . (14.81) Consequently, Rubin’s variance estimator, given by (14.60), when used with “proper” multiple imputation, will converge in expectation to (14.77), which indeed is the asymptotic variance of the “proper” multiple-imputation estimator. Summary 1. Type B or “improper” imputation (i.e., holding the initial estimator ﬁxed across imputations) results in a more eﬃcient estimator than “proper” 0 / ˆ [q{C,GC (Z)}] I var ˆ imputation (where the β s are sampled from N β , for n

n

each imputation); however, this diﬀerence in eﬃciency disappears as m → ∞. 2. Rubin’s variance estimator underestimates the asymptotic variance when used with “improper” imputation. 3. Rubin’s variance estimator correctly estimates the asymptotic variance when used with “proper” imputation (i.e., the variance estimator converges in expectation to the asymptotic variance). As m, the number of imputations, goes to inﬁnity, Rubin’s estimator is also consistent as well as asymptotically unbiased.

14.7 Surrogate Marker Problem Revisited We now return to Example 1, which was introduced at the beginning of the chapter. In this problem, we were interested in estimating the regression parameters θ in a logistic regression model of a binary variable Y as a function of covariates X given by (14.2). However, because X was expensive to measure, a cheaper surrogate variable W for X was also collected and the design was to collect X only on a validation subsample chosen at random with prespeciﬁed probability that depended on Y and W . A surrogate variable W for X is assumed to satisfy the property that p(Y = 1|W, X) = P (Y = 1|X). In addition, we also assume that

(14.82)

372

14 Multiple Imputation: A Frequentist Perspective

X W

∼N

µX µW

σ σ , TXX XW . σXW σW W

Letting R denote the indicator of a complete case (i.e., if R = 1, then we observe (Y, W, X), whereas when R = 0, we only observe (Y, W )), the observed data can be represented as (Ri , Yi , Wi , Ri Xi ), i = 1, . . . , n. We are interested in obtaining an estimator for the parameter β = (θT , µTX , µTW , σXX , σXW , σW W )T using the observed data. We will now illustrate how we would use multiple imputation. First, we need to derive an initial estimator βˆnI . One possibility is to use an inverse weighted complete-case estimator. This is particularly attractive because we know the probability of being included in the validation set by design; that is, P [R = 1|Y, W ] = π(Y, W ). If we had full data, then we could estimate β using standard likelihood estimating equations; namely, n

' ( Xi Yi − expit(θT Xi ) = 0,

i=1 n

(Xi − µX ) = 0,

i=1 n

(Wi − µW ) = 0,

i=1 n '

( (Xi − µX )(Xi − µX )T − σXX = 0,

i=1 n ' i=1 n

(Wi − µW )(Wi − µW )T − σW W

'

(Xi − µX )(Wi − µW )T − σXW

( (

= 0, = 0,

(14.83)

i=1

where expit(u) =

exp(u) . 1 + exp(u)

Using the observed data, an inverse weighted complete-case estimator for β can be obtained by solving the equations

14.7 Surrogate Marker Problem Revisited n i=1 n i=1

373

' ( Ri Xi Yi − expit(θT Xi ) = 0 π(Yi , Wi ) Ri (Xi − µX ) = 0 π(Yi , Wi ) .. .

etc.

(14.84)

Remark 11. If we are interested only in the parameter θ of the logistic regression model, then we only need to solve (14.84) and not rely on any assumption of normality of X, W . However, to use multiple imputation, we need to derive initial estimators for all the parameters. Solving the estimating equations above gives us an initial estimator βˆnI . In addition, using the methods described for IPWCC estimators, say in Chapter 7, we can also derive the inﬂuence function q(R, Y, W, RX) for βˆnI and a consistent estimate of its asymptotic variance var{q(R, ˆ Y, W, RX)}. In order to carry out the imputation, we must be able to sample from the conditional distribution of the full data given the observed data. For our problem, we must sample from the conditional distribution pX|R,Y,W,RX (x|Ri , Yi , Wi , Ri Xi , βˆnI ). Clearly, when Ri = 1 (the complete case), we just use the observed value Xi . However, when Ri = 0, then we must sample from pX|R,Y,W,RX (x|Ri = 0, Yi , Wi , βˆnI ). Because of the MAR assumption, this is the same as pX|Y,W (x|Yi , Wi , βˆnI ). How Do We Sample? We now describe the use of rejection sampling to obtain random draws from the conditional distribution of pX|Y,W (x|Yi , Wi , βˆnI ). Using Bayes’s rule and the surrogacy assumption (14.82), the conditional density is derived as pX|Y,W (x|y, w) =

pY |X (y|x)pX|W (x|w) . pY |X (y|x)pX|W (x|w)dx

Therefore, pX|Y,W (x|y, w) equals

T exp(θˆnI xy)/{1 + exp(θˆnIT x)} pX|W (x, w) , y = 0, 1. normalizing = [numerator] dx constant

374

14 Multiple Imputation: A Frequentist Perspective

Because (X T , W T )T are multivariate normal, this implies that the conditional distribution of X|W is also normally distributed with mean I I −1 E(X|W ) = µ ˆIXn + σ ˆXW [ˆ σW (W − µ ˆIWn ) Wn ] n

(14.85)

and variance T

I I I −1 I var (X|W ) = σ ˆXX −σ ˆXW [ˆ σW σ ˆXWn . Wn ] n n

(14.86)

Therefore, at the j-th imputation, if Ri = 0, we can generate Xij (βˆnI ) by ﬁrst randomly sampling from a normal distribution with mean I I −1 µ ˆIXn + σ ˆXW [ˆ σW (Wi − µ ˆIWn ) Wn ] n

and variance (14.86). After generating such an X in this fashion, we either “keep” this X if another randomly generated uniform random variable is less than exp(θˆnIT XYi ) 1 + exp(θˆIT X) n

or we keep repeating this process until we “keep” an X that we use for the jth imputation Xij (βˆnI ). This rejection sampling scheme guarantees a random draw from pX|Y,W (x|Yi , Wi ). Therefore, at the j-th imputation, together with Yi and Wi , which we always observe, we use Xi if Ri = 1 and Xij (βnI ) if Ri = 0 to create the j-th pseudo∗ full data. This j-th imputed data set is then used to obtain estimators βˆnj as described by (14.83). Standard software packages will do this. The ﬁnal estimate is m βˆ∗ = m−1 βˆ∗ . n

nj

j=1

A consistent estimator of the asymptotic variance can be obtained by substituting consistent estimators for I F (β0 ) and I, say by using (14.66) and (14.67), respectively, and a consistent estimator var{q(R, ˆ Y, W, RX)} for var{q(R, Y, W, RX)} in equation (14.58).

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Index

adaptive semiparametric estimator location-shift regression model, 108–113 monotone coarsening, 243–247 nonmonotone coarsening, 261–265 restricted moment model, 93–97, 286–291 two levels of coarsening, 227–233 aﬃne space, see Hilbert space, linear variety AIPWCC estimator, 148, 178, 180, 199–207, 237 censored survival data, 217 asymptotically linear, 21 asymptotically normal estimator, 8, 41 augmentation space, 174, 183, 201, 332 censored survival data, 216–217 monotone coarsening, 212 projection onto, 273, 333–334 monotone coarsening, 239–243 nonmonotone coarsening, 256–261 two levels of coarsening, 226–227 augmented inverse probability weighted complete-case estimator, see AIPWCC estimator auxiliary variables, 133–135 average causal treatment eﬀect, 323–337 augmented inverse propensity weighted estimator, 337 regression modeling, 328–329 Cauchy-Schwartz inequality, 19 causal inference, 323

censored survival data, 115, 212–218, 254–255 coarsened data, 152 likelihood, 156–163 monotone coarsening, 186–188, 195 two levels of coarsening, 185–186 coarsening at random (CAR), 155, 158–163, 181 coarsening by design, 159, 165 coarsening completely at random (CCAR), 154, 181 coarsening model tangent space, 190–192, 202 coarsening probability, 155, 165, 182, 195 coarsening probability model, 159 monotone coarsening, 186–188, 195, 211 two levels of coarsening, 185 coarsening process, see coarsening probability model coarsening variable, 152 complete data, 139, 155 consistent estimator, 8 contiguity theory, 34–36 contraction mapping, 258, 292 counterfactual random variable, see potential outcome counting process, 117, 119, 216 covariate adjustment, 101, 126–133 double robust estimator, 147–150, 239, 273, 313–321, 336–337 monotone coarsening, 248–251

382

Index

nonmonotone coarsening, 265–267 two levels of coarsening, 234–236 eﬃcient estimator, 24, 360 proportional hazards model, 125 eﬃcient inﬂuence function, 42–48, 50, 65, 347, 360 proportional hazards model, 125 restricted moment model, 85–87 eﬃcient score vector, 47, 50, 64, 164 coarsened data, 277–282 location-shift regression model, 107–108 proportional hazards model, 125 restricted moment model, 86 EM algorithm, 358 full data, 139, 155 full-data Hilbert space, 163, 181 generalized estimating equation (GEE), 9, 145 for restricted moment model, 54–58, 93 globally eﬃcient, 63, 125 Hilbert space, 13–14 direct sum of linear subspaces, 42, 49 inner product, 13 covariance inner product, 13, 49 inverse operator, 170, 258–261, 282 linear mapping, 169 linear subspace, 14 linear variety, 45, 67, 222 orthogonal, 13 orthogonal complement, 42 projection onto a linear subspace, 43 projection theorem, 14 Pythagorean theorem, 14 multivariate, 44 replicating linear space, 44, 296 space of mean-zero q-dimensional random functions, 11, 16–18 improper imputation, 346 imputation, 144–146 inﬁnite dimensional, 2–3 inﬂuence function, 22, 49, 61–68, 164, 347

coarsened data, 168, 193–196, 202–206, 329–333 double robust, 221–225, 273 restricted moment model, 83–85 information matrix, 32, 340, 342–344 inverse operator, see Hilbert space, inverse operator inverse probability weighted completecase estimator, see IPWCC estimator IPWCC estimator, 146–147, 178, 180, 236 IPWCC space, 174, 183 linear mapping, see Hilbert space, linear mapping linear model, 4, 111–113 linear operator, see Hilbert space, linear mapping linear space, 2 linear variety, see Hilbert space, linear variety local data generating process (LDGP), 26 locally eﬃcient, 63, 94, 108, 273–292, 335, 337 location-shift regression model, 101–113 log-linear model, 4, 96–97, 309–313 logistic regression model, 88, 95–96, 147, 162–163, 179–181, 185, 236–239, 319–321, 341, 371–374 m estimator, 29–31, 200 martingale process, 117, 119, 122, 217 maximum likelihood estimator (MLE), 24, 48, 96, 188–189, 340 maximum partial likelihood estimator, 9 mean-square closure, 63 missing at random (MAR), 142, 151 missing by design, see coarsening by design missing completely at random (MCAR), 140, 151 missing data likelihood, 143–144 multiple imputation, 339–374 no unmeasured confounders, 327 noncoarsening at random (NCAR), 155, 181

Index nonmissing at random (NMAR), 141, 151 nonmonotone coarsened data, 188, 255–267 nonparametric model, 3, 8, 125–126, 275 nuisance parameter, 2, 21, 41, 53, 59, 67, 101, 116, 119, 152, 156, 168, 190, 226, 297, 339 nuisance tangent space coarsened data, 165–174, 182, 190–193 full data, 164, 181 location-shift regression model, 103–105 parametric model, 38, 49 parametric submodel, 61 proportional hazards model, 117–120 restricted moment model, 77–83 semiparametric model, 63–64 observational study, 327 observed data, 139, 155 observed-data Hilbert space, 164 optimal restricted AIPWCC estimator, 295–321 class 1, 300–313 class 2, 313–321 orthogonal, see Hilbert space, orthogonal orthogonal complement, see Hilbert space, orthogonal complement parametric model, 1, 21, 339 parametric submodel, 59–61 location-shift regression model, 104 proportional hazards model, 60, 118, 119 restricted moment model, 75, 78, 80, 88, 90 point exposure study, 323 posterior distribution, 346, 366 potential outcome, 324

383

pretest-posttest study, 101, 126–133, 135 prior distribution, 346 projection theorem, see Hilbert space, projection theorem propensity score, 330, 335, 337 proper imputation, 366–371 proportional hazards model, 7–8, 113–125, 218 Pythagorean theorem, see Hilbert space, Pythagorean theorem randomization and causality, 326–327 regular asymptotically linear estimator (RAL), 27, 168 regular estimator, 26–27 restricted moment model, 3–7, 73–98, 174–179 longitudinal data, 210–212, 251–253 sandwich variance estimator, 31–32, 206–207, 233–234 score vector, 27, 49, 340 coarsened data, 166–168 semiparametric eﬃciency bound, 63 semiparametric estimator, 8 for restricted moment model, 54–58 location-shift regression model, 106–107 proportional hazards model, 123–124 semiparametric model, 2, 53 stable unit treatment value assumption (SUTVA), 325 statistical model, 1 strong ignorability assumption, see no unmeasured confounders successive approximation, 258 super-eﬃciency, 24–26 surrogate marker problem, 341, 371–374 tangent space nonparametric model, 68–69 parametric model, 38, 49 semiparametric model, 67

Springer Series in Statistics

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